A method and related device for evaluating pure tone annoyance of a high frequency transformer

Abstract

The application provides a high-frequency transformer pure tone noise annoyance evaluation method and related device, and belongs to the field of acoustics and noise control. The application identifies significant harmonic pure tones by first collecting noise data of the high-frequency transformer under different working conditions and performing peak value detection. Secondly, audible quantification calculation is performed based on the data of the significant harmonic pure tones, and the subjective correction value of each pure tone is determined in combination with the audible-subjective correction value relationship constructed in each preset frequency band. Thirdly, the comprehensive correction sound pressure level is obtained by superimposing and calculating the sound pressure level of each single pure tone in the same frequency band. Finally, the final subjective score is obtained by using a multiple regression model with the comprehensive correction sound pressure level of each frequency band as the independent variable and the subjective annoyance score as the dependent variable. The model is constructed by fitting and verifying the standardized samples. The scheme of the application realizes accurate evaluation of the subjective annoyance of the pure tone noise of the high-frequency transformer, and improves the scientificity and practicality of the noise evaluation of the high-frequency transformer.

Description

Technical Field

[0001] This invention belongs to the field of acoustics and noise control technology, specifically relating to a method and related apparatus for evaluating the annoyance level of pure-tone noise in high-frequency transformers. Background Technology

[0002] With the rapid rise of emerging industries such as new energy vehicles and energy storage systems, solid-state converters (SSTs), as devices for achieving efficient power conversion, have been widely used in the power sector. In SSTs, the isolated DC-DC converter containing a high-frequency transformer is the main component, playing a crucial role in achieving electrical isolation, voltage matching, and improving efficiency.

[0003] In pursuing the goal of minimizing the size of high-frequency transformers, the optimal switching frequency of some high-frequency transformers is only in the range of a few kilohertz, which leads to significant audible noise generation. The noise generated during the operation of high-frequency transformers not only affects workers who are exposed at close range for a long time, but may also interfere with the normal life of surrounding residents, causing health risks such as hearing loss and sleep disorders, while also disturbing the surrounding environment.

[0004] The noise energy of high-frequency transformers largely originates from pure-tone noise and exhibits significant multi-harmonic characteristics, which exacerbate subjective discomfort. However, existing high-frequency transformer noise assessment technologies have several shortcomings. First, the assessment indicators lack adaptability. Current transformer noise assessments generally use A-weighted sound pressure level as the sole indicator, but this indicator is primarily designed for broadband frequency noise. For noise with pure-tone characteristics, using A-weighting underestimates the subjective discomfort it causes and fails to accurately reflect the actual impact of high-frequency multi-harmonic pure tones on the human body. Second, direct application of international standards is ineffective. While ISO / TS20065:2022 provides an objective evaluation method for pure-tone audibility, it does not address the correction logic for subjective discomfort, making it difficult to establish a connection between objective indicators and subjective perception. Third, subjective correction rules are lacking. Existing technologies have not established a systematic subjective correction system for high-frequency transformer multi-harmonic pure tones. They fail to consider the masking effect of background noise types on pure-tone perception in the application scenario, lack comprehensive correction rules for multi-frequency superposition, and lack a process for determining correction rules through standardized psychoacoustic experiments. Summary of the Invention

[0005] Therefore, it is necessary to provide a method and related device for evaluating the annoyance level of pure-tone noise of high-frequency transformers, in order to address the shortcomings of traditional noise evaluation methods that cannot accurately reflect the subjective discomfort of high-frequency transformers, and to achieve an accurate evaluation of the subjective annoyance level of pure-tone noise of high-frequency transformers.

[0006] In a first aspect, the present invention provides a method for evaluating the annoyance level of pure-tone noise in a high-frequency transformer, comprising the following steps:

[0007] Noise data of high-frequency transformers under different operating conditions was collected to obtain noise acquisition data;

[0008] Peak detection is performed on the noise acquisition data to identify all significant harmonic pure tones that meet the preset peak determination conditions, and significant harmonic pure tone data is obtained.

[0009] Based on the significant harmonic pure tone data, the audibility of each significant harmonic pure tone is quantitatively calculated to obtain the audibility of each significant harmonic pure tone.

[0010] Based on audibility, the subjective correction value of each significant harmonic pure tone is determined from the pre-constructed audibility-subjective correction value relationship; the audibility-subjective correction value relationship is constructed separately for multiple preset frequency bands and is used to represent the relationship between the audibility of each harmonic in the corresponding frequency band and the corresponding subjective correction value; the subjective correction value is the difference between the A-weighted sound pressure level of the reference noise sample and the measured A-weighted sound pressure level of the high-frequency transformer noise sample under the same subjective evaluation conditions;

[0011] For each preset frequency band, the A-weighted sound pressure level of each significant harmonic pure tone in the frequency band is added to the corresponding subjective correction value to obtain the simple tone corrected sound pressure level. All simple tone corrected sound pressure levels in the same frequency band are superimposed to obtain the comprehensive corrected sound pressure level of the corresponding frequency band.

[0012] Based on the comprehensive corrected sound pressure level, the corresponding subjective score is obtained using a pre-constructed multiple regression model based on the corrected sound pressure level and the subjective annoyance score. The multiple regression model is a multiple regression model fitted with the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable.

[0013] Furthermore, peak detection is performed on the noise acquisition data to identify all significant harmonic pure tones that meet the preset peak determination criteria, and significant harmonic pure tone data is obtained, including:

[0014] The noise acquisition data was analyzed using the peak frequency detection method to obtain the spectrum analysis results.

[0015] Based on the preset peak determination conditions, the spectrum analysis results are screened, and acoustic signals whose peak signals meet the threshold difference requirements are extracted. The acoustic signals are then used as significant harmonic pure tones.

[0016] The frequency parameters and sound pressure power spectral density parameters of each significant harmonic pure tone are extracted and integrated to obtain significant harmonic pure tone data.

[0017] Furthermore, the pre-construction process for the audibility-subjective correction value relationship includes:

[0018] Broadband noise with the same frequency coverage as the high-frequency transformer noise sample was selected as the reference noise sample, and a linear correlation was established between the reference A-weighted sound pressure level of the reference sample and the subjective annoyance score.

[0019] The measured A-weighted sound pressure level and subjective annoyance score of high-frequency transformer noise samples were obtained, and the baseline A-weighted sound pressure level corresponding to the subjective annoyance score of the high-frequency transformer noise samples was matched using linear correlation.

[0020] The difference between the matched baseline A-weighted sound pressure level and the measured A-weighted sound pressure level is used as the comprehensive subjective correction value for the corresponding sample.

[0021] Based on the proportion of the acoustic power spectral density of each harmonic in the high-frequency transformer noise sample, the comprehensive subjective correction value is decomposed to obtain the independent subjective correction value corresponding to each harmonic.

[0022] Multiple preset frequency bands are divided according to the preset harmonic frequency range division rules, and the audibility of each harmonic in the same frequency band is fitted with the independent subjective correction value to obtain the audibility-subjective correction value relationship of the corresponding frequency band.

[0023] Furthermore, the corrected sound pressure levels of all simple tones within the same frequency band are superimposed to obtain the comprehensive corrected sound pressure level for the corresponding frequency band, including:

[0024] According to the preset frequency band division rules, all simple tone corrected sound pressure levels are divided into their respective preset frequency bands;

[0025] The sound pressure level energy conversion process is performed sequentially on all simple tone corrected sound pressure levels within the same preset frequency band, and the converted energy data of all the same frequency band are accumulated and integrated to obtain the frequency band energy.

[0026] Logarithmic conversion of frequency band energy is performed to obtain the comprehensive corrected sound pressure level for the corresponding preset frequency band.

[0027] Furthermore, the pre-construction process of the multiple regression model based on the modified sound pressure level-annoyance subjective score includes:

[0028] All high-frequency transformer noise samples were divided into independent training sample groups and validation sample groups.

[0029] The comprehensive corrected sound pressure level of each noise sample in the training sample group is obtained in all preset frequency bands. The comprehensive corrected sound pressure level corresponding to each preset frequency band is used as the independent variable, and the subjective annoyance score of each noise sample is used as the dependent variable. A multiple regression model is constructed and fitted.

[0030] The multiple regression model is validated using a validation sample set. Once the validation is successful, the final multiple regression model is obtained.

[0031] Furthermore, the process for determining the subjective annoyance level score includes:

[0032] Select evaluation subjects that meet the hearing requirements and set a unified scoring metric.

[0033] Under a fixed acoustic test environment, high-frequency transformer noise samples and reference noise samples are played in sequence. The evaluation subject evaluates each sample according to the scoring metric standard to obtain the initial score data for each noise sample.

[0034] The initial scoring data were subjected to correlation statistical analysis to remove abnormal scoring data that deviated from the overall evaluation pattern, and the remaining scoring data were taken as valid scoring data.

[0035] After normalizing the valid scoring data, the subjective annoyance score corresponding to each noise sample is obtained.

[0036] Secondly, the present invention provides a device for evaluating the annoyance level of pure-tone noise in a high-frequency transformer, comprising:

[0037] The noise acquisition module is used to collect noise data of high-frequency transformers under different operating conditions to obtain noise acquisition data;

[0038] The pure tone recognition module is used to perform peak detection on noise acquisition data, identify all significant harmonic pure tones that meet the preset peak determination conditions, and obtain significant harmonic pure tone data.

[0039] The pure tone audibility calculation module is used to perform audibility quantification calculation on each significant harmonic pure tone based on significant harmonic pure tone data, and obtain the audibility of each significant harmonic pure tone;

[0040] The subjective correction value determination module is used to determine the subjective correction value of each significant harmonic pure tone based on audibility from a pre-constructed audibility-subjective correction value relationship. The audibility-subjective correction value relationship is constructed separately for multiple preset frequency bands and is used to represent the relationship between the audibility of each harmonic in the corresponding frequency band and the corresponding subjective correction value. The subjective correction value is the difference between the A-weighted sound pressure level of the reference noise sample and the A-weighted sound pressure level of the measured high-frequency transformer noise sample under the same subjective evaluation conditions.

[0041] The modified sound pressure level calculation module is used to add the A-weighted sound pressure level of each significant harmonic pure tone in each preset frequency band to the corresponding subjective correction value to obtain the modified sound pressure level of a simple tone, and to superimpose all the modified sound pressure levels of simple tones in the same frequency band to obtain the comprehensive modified sound pressure level of the corresponding frequency band.

[0042] The annoyance assessment module is used to obtain the corresponding subjective score based on the comprehensive corrected sound pressure level using a pre-constructed multiple regression model based on the corrected sound pressure level and the annoyance subjective score. The multiple regression model is a multiple regression model fitted with the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable.

[0043] Thirdly, the present invention provides a computer device, the device including a processor and a memory:

[0044] The memory is used to store computer programs and send the instructions of the computer programs to the processor;

[0045] The processor executes the following steps according to the instructions of the computer program:

[0046] Noise data of high-frequency transformers under different operating conditions was collected to obtain noise acquisition data;

[0047] Peak detection is performed on the noise acquisition data to identify all significant harmonic pure tones that meet the preset peak determination conditions, and significant harmonic pure tone data is obtained.

[0048] Based on the significant harmonic pure tone data, the audibility of each significant harmonic pure tone is quantitatively calculated to obtain the audibility of each significant harmonic pure tone.

[0049] Based on audibility, the subjective correction value of each significant harmonic pure tone is determined from the pre-constructed audibility-subjective correction value relationship; the audibility-subjective correction value relationship is constructed separately for multiple preset frequency bands and is used to represent the relationship between the audibility of each harmonic in the corresponding frequency band and the corresponding subjective correction value; the subjective correction value is the difference between the A-weighted sound pressure level of the reference noise sample and the measured A-weighted sound pressure level of the high-frequency transformer noise sample under the same subjective evaluation conditions;

[0050] For each preset frequency band, the A-weighted sound pressure level of each significant harmonic pure tone in the frequency band is added to the corresponding subjective correction value to obtain the simple tone corrected sound pressure level. All simple tone corrected sound pressure levels in the same frequency band are superimposed to obtain the comprehensive corrected sound pressure level of the corresponding frequency band.

[0051] Based on the comprehensive corrected sound pressure level, the corresponding subjective score is obtained using a pre-constructed multiple regression model based on the corrected sound pressure level and the subjective annoyance score. The multiple regression model is a multiple regression model fitted with the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable.

[0052] Fourthly, the present invention provides a computer-readable storage medium on which a computer program is stored, and when executed by a processor, the computer program performs the following steps:

[0053] Noise data of high-frequency transformers under different operating conditions was collected to obtain noise acquisition data;

[0054] Peak detection is performed on the noise acquisition data to identify all significant harmonic pure tones that meet the preset peak determination conditions, and significant harmonic pure tone data is obtained.

[0055] Based on the significant harmonic pure tone data, the audibility of each significant harmonic pure tone is quantitatively calculated to obtain the audibility of each significant harmonic pure tone.

[0056] Based on audibility, the subjective correction value of each significant harmonic pure tone is determined from the pre-constructed audibility-subjective correction value relationship; the audibility-subjective correction value relationship is constructed separately for multiple preset frequency bands and is used to represent the relationship between the audibility of each harmonic in the corresponding frequency band and the corresponding subjective correction value; the subjective correction value is the difference between the A-weighted sound pressure level of the reference noise sample and the measured A-weighted sound pressure level of the high-frequency transformer noise sample under the same subjective evaluation conditions;

[0057] For each preset frequency band, the A-weighted sound pressure level of each significant harmonic pure tone in the frequency band is added to the corresponding subjective correction value to obtain the simple tone corrected sound pressure level. All simple tone corrected sound pressure levels in the same frequency band are superimposed to obtain the comprehensive corrected sound pressure level of the corresponding frequency band.

[0058] Based on the comprehensive corrected sound pressure level, the corresponding subjective score is obtained using a pre-constructed multiple regression model based on the corrected sound pressure level and the subjective annoyance score. The multiple regression model is a multiple regression model fitted with the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable.

[0059] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0060] Noise data of high-frequency transformers under different operating conditions was collected to obtain noise acquisition data;

[0061] Peak detection is performed on the noise acquisition data to identify all significant harmonic pure tones that meet the preset peak determination conditions, and significant harmonic pure tone data is obtained.

[0062] Based on the significant harmonic pure tone data, the audibility of each significant harmonic pure tone is quantitatively calculated to obtain the audibility of each significant harmonic pure tone.

[0063] Based on audibility, the subjective correction value of each significant harmonic pure tone is determined from the pre-constructed audibility-subjective correction value relationship; the audibility-subjective correction value relationship is constructed separately for multiple preset frequency bands and is used to represent the relationship between the audibility of each harmonic in the corresponding frequency band and the corresponding subjective correction value; the subjective correction value is the difference between the A-weighted sound pressure level of the reference noise sample and the measured A-weighted sound pressure level of the high-frequency transformer noise sample under the same subjective evaluation conditions;

[0064] For each preset frequency band, the A-weighted sound pressure level of each significant harmonic pure tone in the frequency band is added to the corresponding subjective correction value to obtain the simple tone corrected sound pressure level. All simple tone corrected sound pressure levels in the same frequency band are superimposed to obtain the comprehensive corrected sound pressure level of the corresponding frequency band.

[0065] Based on the comprehensive corrected sound pressure level, the corresponding subjective score is obtained using a pre-constructed multiple regression model based on the corrected sound pressure level and the subjective annoyance score. The multiple regression model is a multiple regression model fitted with the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable.

[0066] In summary, this invention provides a method and related apparatus for evaluating the annoyance of pure-tone noise in high-frequency transformers. First, noise data from the high-frequency transformer under different operating conditions is collected and peak values ​​are detected to identify significant harmonic pure tones and their corresponding data that meet preset conditions. Second, audibility is quantitatively calculated based on the significant harmonic pure-tone data, and the subjective correction value for each pure tone is determined by combining the audibility-subjective correction value relationship constructed according to preset frequency bands. The subjective correction value is determined by the A-weighted sound pressure level difference between the reference noise sample and the measured noise sample. This not only compensates for the deficiency of the ISO / TS20065:2022 standard in not addressing subjective discomfort correction logic, but also establishes a correlation between objective audibility indicators and subjective perception. Furthermore, the design of the frequency band-based relationship considers the impact of pure tones in different frequency bands on... The invention addresses the issue of existing technologies lacking a systematic subjective correction mechanism by considering the differences in subjective human perception. Furthermore, it improves the comprehensive correction sound pressure level by superimposing the corrected sound pressure levels of each simple tone within the same frequency band, thus refining the comprehensive correction rules for multi-frequency superposition. This avoids evaluation biases caused by neglecting the frequency superposition effect in single-tone corrections and avoids the drawback of A-weighted sound pressure levels underestimating subjective discomfort for pure-tone noise. Finally, it uses a multiple regression model with the comprehensive corrected sound pressure level of each frequency band as the independent variable and the subjective annoyance score as the dependent variable to obtain the final subjective score. This model, constructed through standardized sample fitting and validation, forms a standardized psychoacoustic evaluation process, solving problems such as the lack of standardized correction rule determination procedures and the failure to consider background noise masking effects in existing technologies. This invention achieves accurate evaluation of the subjective annoyance of high-frequency transformer pure-tone noise, effectively reflecting the actual subjective discomfort of high-frequency multi-harmonic pure tones on the human body, and improving the scientific rigor and practicality of high-frequency transformer noise evaluation. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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 effort.

[0068] Figure 1 This is a flowchart illustrating a method for evaluating the annoyance level of pure-tone noise in a high-frequency transformer, according to one embodiment of the present invention.

[0069] Figure 2 This is a block diagram of a high-frequency transformer pure-tone noise annoyance evaluation device according to one embodiment of the present invention;

[0070] Figure 3 This is a block diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0071] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0072] To facilitate understanding of the technical content of this invention and to clarify the meaning of the technical terms involved in the subsequent description, some related terms are defined and explained as follows.

[0073] High-frequency transformers: These are electrical energy conversion and transmission devices that operate at frequencies much higher than the traditional power frequency (50Hz), typically in the kHz~MHz frequency band. They are important components in new power systems.

[0074] Pure-tone noise, referring to discrete single-frequency noise, is a fundamental term in the fields of acoustics and noise control. It presents as a clear, sharp, and piercing single-frequency sound with high distinguishability, easily causing auditory annoyance and is a key type of undesirable noise in sound quality evaluation. In power equipment such as transformers, excitations such as core magnetostriction and electromagnetic resonance easily generate power frequency and its harmonics pure-tone noise, which is a key target for equipment vibration and noise control.

[0075] Pure tone audibility (TA): Used to quantitatively describe the degree to which discrete pure tone components in noise are perceived and discernible by the human ear in broadband background noise.

[0076] Subjective evaluation is an experimental method that takes human auditory perception as its core and conducts qualitative or quantitative assessments of the psychoacoustic feelings of sound / noise, such as annoyance and acceptability, through the evaluation subject. It is a key means of connecting objective acoustic parameters with real human auditory experience and is also a core part of sound quality research.

[0077] The background technology will be further introduced.

[0078] With the rapid rise of emerging industries such as new energy vehicles and energy storage systems, the efficiency, power density, and reliability of power conversion have become core demands for industrial development. Solid-state converters (SSTs), as key equipment for achieving high-efficiency power conversion, have been widely used in various emerging fields. Isolated DC-DC converters are the core components of SSTs, and high-frequency transformers are the core components in these converters that achieve electrical isolation, voltage matching, and efficiency improvement. Therefore, many studies focus on the optimized design of high-frequency transformers to meet the industry's development requirements for "high efficiency, high power density, and high reliability."

[0079] In the optimization process of high-frequency transformers, minimizing size has become one of the important design goals. To achieve this goal, the optimal switching frequency of some high-frequency transformers has been set to several kilohertz. This frequency range directly leads to significant audible noise generation. According to relevant reports, noise has been listed as an emerging environmental problem, posing a global public health threat. The noise generated during the operation of high-frequency transformers not only adversely affects workers who are exposed to it at close range for a long time, but may also interfere with the normal lives of nearby residents, causing a series of health risks such as hearing loss and sleep disorders, while also causing serious environmental disturbances. Therefore, the scientific and accurate evaluation of the noise of high-frequency transformers has become an important issue that urgently needs to be addressed in the current industrial development.

[0080] Research has revealed that the noise energy of high-frequency transformers almost entirely originates from pure-tone noise and exhibits significant multi-harmonic characteristics. Specifically, with the operating frequency as the fundamental frequency, there is significant single-frequency noise at harmonic frequencies such as the 2nd, 3rd, and 4th harmonics, forming a complex noise characteristic of multiple frequency pure tones superimposed. This multi-harmonic superposition characteristic further exacerbates the subjective discomfort experienced by the human body. Compared to ordinary broadband frequency noise, it is more likely to induce negative emotions such as irritability and anxiety, which further increases the difficulty of evaluating the noise of high-frequency transformers.

[0081] Currently, some technical solutions and standards exist in the industry related to noise assessment of high-frequency transformers. Some of these solutions attempt to overcome the inaccuracies of traditional frequency-weighted assessment methods by preprocessing and modal decomposition of noise data, combined with trained evaluation models. Other standards document the classification and calculation models for noise annoyance levels, providing some reference for subjective noise assessment. However, these existing technologies and standards still have many core shortcomings and cannot meet the actual needs of subjective discomfort assessment of multi-harmonic pure-tone noise from high-frequency transformers.

[0082] Specifically, the core defects of existing technologies are mainly reflected in three aspects: First, the evaluation indicators are not adaptable enough. Currently, transformer noise evaluation generally uses A-weighted sound pressure level as the only evaluation indicator. However, this indicator is mainly designed for broadband frequency noise. Numerous studies have confirmed that for high-frequency multi-harmonic noise with pure tone characteristics, using A-weighted sound pressure level will underestimate the subjective discomfort it causes to the human body and cannot accurately reflect the actual impact of noise on the human body. Second, the direct application of relevant standards is ineffective. Existing relevant standards only provide objective evaluation methods for the audibility of pure tones, without involving the correction logic for subjective discomfort, and cannot establish an effective correlation between objective evaluation indicators and human subjective feelings. Third, subjective correction rules are lacking. Existing technologies have not established a systematic subjective correction system for the characteristics of high-frequency transformer multi-harmonic pure tones. They have not considered the masking effect of background noise on pure tone perception in its application scenarios, nor have they designed comprehensive correction rules for the superposition of multiple frequency pure tones, and they lack a standardized process for determining correction rules through standardized psychoacoustic experiments.

[0083] In summary, existing noise assessment technologies cannot accurately reflect the subjective discomfort caused to the human body by the multi-harmonic pure tone noise of high-frequency transformers, and are difficult to meet the industry's need for scientific and accurate evaluation of high-frequency transformer noise. There is an urgent need for a high-frequency transformer pure tone noise assessment technology that is adaptable, practical and engineering operable. Against this background, this invention proposes a method and related device for evaluating the annoyance level of high-frequency transformer pure tone noise. The following is a detailed description of the various embodiments of this invention.

[0084] Please see Figure 1 This invention provides a method for evaluating the annoyance level of pure-tone noise in high-frequency transformers, comprising the following steps:

[0085] S101: Collect noise data of high-frequency transformers under different operating conditions to obtain noise acquisition data.

[0086] Among them, noise acquisition data refers to noise-related data collected under multiple operating conditions and multiple measurement points of high-frequency transformers through standardized testing methods, including information such as sound pressure level and spectrum.

[0087] Optionally, noise data of high-frequency transformers under multiple operating conditions and multiple measurement points can be collected according to standardized testing methods, covering different operating frequencies, different voltage levels, and different operating modes.

[0088] For example, a standard sound source is used to calibrate the high-frequency response microphone (frequency response 20Hz-20kHz, total harmonic distortion <0.5%) and data acquisition equipment, with a sensitivity error ≤±0.2dB. Near-field measurement points are located 1m from the transformer body, with six points arranged at horizontal angles of 0°, 90°, 180°, and 270°, and vertical angles of +45° and -45°, covering high-frequency pure tone directivity. Far-field measurement points are located 1m above the boundary of the equipment installation environment, with three points evenly distributed. Noise acquisition must be performed under different operating conditions, including different operating frequencies (e.g., 4kHz, 5kHz, 8kHz…), different voltage levels (e.g., 300V, 500V, 700V…), and different operating modes (no load, 50% load, 75% load…), with noise acquisition for at least 30 seconds under each condition.

[0089] S102: Perform peak detection on the noise acquisition data, identify all significant harmonic pure tones that meet the preset peak determination conditions, and obtain significant harmonic pure tone data.

[0090] Among them, significant harmonic pure tone refers to the harmonic components in the noise acquisition data whose peak values ​​meet the preset judgment conditions and can be effectively identified; significant harmonic pure tone data includes information such as the frequency parameters and sound pressure power spectral density of each significant harmonic pure tone.

[0091] Optionally, the collected high-frequency transformer noise data can be used as input parameters. A peak frequency detection algorithm can be employed, with a peak threshold set (more than 6 dB above adjacent frequency bands; the identification logic refers to ISO / TS 20065:2022), to identify all significant harmonic pure frequency frequencies f1, f2, ..., f... n .

[0092] S103: Based on the significant harmonic pure tone data, the audibility of each significant harmonic pure tone is quantitatively calculated to obtain the audibility of each significant harmonic pure tone.

[0093] Among them, audibility (TA) is an indicator that quantifies the degree to which a harmonic pure tone can be perceived by the human ear and is used to determine whether a pure tone is masked by background noise.

[0094] Optionally, referring to the logic of ISO / TS 20065:2022, the audibility of each identified harmonic pure tone is calculated separately, and the TA of a single harmonic pure tone is determined. i At ≥0dB, the pure tone at this frequency can be clearly perceived. i When the audibility of the pure tone at a frequency is less than 0 dB, it is masked by background noise and has no obvious subjective perception. The set of pure tone audibility for each harmonic is output as the result.

[0095] For example, following the logic of ISO / TS 20065:2022, the audibility of each identified harmonic pure tone is calculated separately, resulting in the audibility set of each harmonic pure tone {TA={TA1,TA2,...,TA...}}. n}}.

[0096] S104: Based on audibility, determine the subjective correction value of each significant harmonic pure tone from the pre-constructed audibility-subjective correction value relationship; the audibility-subjective correction value relationship is constructed separately according to multiple preset frequency bands, and is used to represent the relationship between the audibility of each harmonic in the corresponding frequency band and the corresponding subjective correction value; the subjective correction value is the difference between the A-weighted sound pressure level of the reference noise sample and the measured A-weighted sound pressure level of the high-frequency transformer noise sample under the same subjective evaluation conditions.

[0097] The audibility-subjective correction value (KT-TA) relationship is constructed separately according to preset frequency bands, reflecting the correlation between harmonic audibility and subjective correction value in the corresponding frequency band; the subjective correction value (KT) is a parameter used to correct the objective sound pressure level of pure tone and conform to the subjective perception of human ear.

[0098] Optionally, the principle of subjective correction value is to make up for the deviation between the objective sound pressure level of the pure tone and subjective perception. Since the human ear has different sensitivities to pure tones of different frequency bands and different audibility, by pre-constructing the correlation between audibility and correction value, the corresponding correction parameters can be quickly matched according to the audibility of the pure tone, so that the objective parameters are more in line with the subjective feeling.

[0099] S105: For each preset frequency band, the A-weighted sound pressure level of each significant harmonic pure tone in the frequency band is added to the corresponding subjective correction value to obtain the simple tone corrected sound pressure level. All simple tone corrected sound pressure levels in the same frequency band are superimposed to obtain the comprehensive corrected sound pressure level of the corresponding frequency band.

[0100] Among them, the simple tone corrected sound pressure level is used to correct the deviation between the objective sound pressure level of the pure tone and the subjective perception, while the comprehensive corrected sound pressure level reflects the comprehensive contribution of the pure tone noise in this frequency band to the overall annoyance level.

[0101] Optionally, this step can first calibrate the objective sound pressure level of a single pure tone using subjective correction values, and then, based on the principle of acoustic energy superposition, integrate the corrected sound pressure levels of multiple pure tones within the same frequency band to obtain comprehensive parameters that reflect the overall impact of the frequency band.

[0102] S106: Based on the comprehensive corrected sound pressure level, the corresponding subjective score is obtained using a pre-constructed multiple regression model based on the corrected sound pressure level and the subjective annoyance score. The multiple regression model is a multiple regression model fitted with the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable.

[0103] Among them, the multiple regression model is constructed by fitting the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable, which can achieve accurate prediction of the overall annoyance of pure tone noise; the subjective score reflects the subjective annoyance of the human ear to the noise.

[0104] Optionally, this step can establish a quantitative correlation between the comprehensive corrected sound pressure level of each frequency band and the subjective annoyance score through multiple regression analysis, and use statistical methods to fit a prediction model to realize the transformation from objective correction parameters to subjective feeling scores, thus ensuring the accuracy and objectivity of annoyance evaluation.

[0105] This embodiment first collects noise data from a high-frequency transformer under different operating conditions and performs peak detection to identify significant harmonic pure tones and their corresponding data that meet preset conditions. Then, based on the significant harmonic pure tone data, it performs audibility quantitative calculations and determines the subjective correction value for each pure tone by combining the audibility-subjective correction value relationship constructed according to preset frequency bands. The subjective correction value is determined by the A-weighted sound pressure level difference between the reference noise sample and the measured noise sample. This not only compensates for the deficiency in the ISO / TS20065:2022 standard that does not address subjective discomfort correction logic, but also establishes a correlation between objective audibility indicators and subjective perception. Furthermore, the design of constructing the relationship according to frequency bands considers the different impacts of pure tones in different frequency bands on human subjective perception, thus solving... This paper addresses the problem that existing technologies lack a systematic subjective correction framework. Secondly, by superimposing the corrected sound pressure levels of each simple tone within the same frequency band to obtain the comprehensive corrected sound pressure level, it improves the comprehensive correction rules for multi-frequency superposition, avoiding evaluation bias caused by neglecting the frequency superposition effect in single-tone correction, and also circumventing the drawback of A-weighted sound pressure levels underestimating subjective discomfort for pure-tone noise. Finally, a multiple regression model with the comprehensive corrected sound pressure level of each frequency band as the independent variable and the subjective annoyance score as the dependent variable is used to obtain the final subjective score. This model is constructed through standardized sample fitting and verification, forming a standardized psychoacoustic evaluation process, solving problems such as the lack of a standardized correction rule determination process and the failure to consider the background noise masking effect in existing technologies.

[0106] In an exemplary embodiment, peak detection is performed on the noise acquisition data to identify all significant harmonic pure tones that meet preset peak determination conditions, and significant harmonic pure tone data is obtained, including:

[0107] S201: The noise acquisition data is analyzed by using the peak frequency detection method to obtain the spectrum analysis results.

[0108] Among them, the peak detection method is an algorithm used to identify peak signals from the noise spectrum. Its core is to capture signals with obvious peak characteristics by analyzing the spectral distribution of noise data. The spectrum analysis result is obtained by decomposing the noise acquisition data into a spectrum, which includes information such as the sound pressure power spectral density of noise at different frequencies.

[0109] For example, using the collected high-frequency transformer noise data as input parameters, a spectrum peak detection algorithm is used to perform a comprehensive spectrum analysis on the noise data, decompose the sound pressure power spectral density of the noise at different frequencies (covering 20Hz-20kHz, which fits the frequency response range of a high-frequency response microphone), and obtain complete spectrum analysis results.

[0110] S202: Based on the preset peak determination conditions, the spectrum analysis results are screened, the acoustic signals whose peak signals meet the threshold difference requirements are extracted, and the acoustic signals are used as significant harmonic pure tones.

[0111] Among them, the preset peak determination condition is a standard used to distinguish significant harmonic pure tone from ordinary noise signal. The core is the sound pressure level difference threshold between the peak signal and adjacent frequency bands. Significant harmonic pure tone refers to the harmonic component of the peak signal that meets the determination condition and can be effectively perceived by the human ear, which is different from non-significant signals that are masked by background noise.

[0112] For example, a peak threshold is set (more than 6dB above the adjacent frequency band, with the identification logic referring to ISO / TS 20065:2022). Based on this threshold, the peak signals in the spectrum analysis results are filtered, and acoustic signals with peak values ​​more than 6dB above the adjacent frequency band are extracted and identified as significant harmonic pure tones.

[0113] S203: Extract the frequency parameters and sound pressure power spectral density parameters of each significant harmonic pure tone, and integrate and summarize them to obtain significant harmonic pure tone data.

[0114] Among them, the frequency parameter is a characteristic parameter of significant harmonic pure tone, which represents the frequency magnitude of the pure tone; the sound pressure power spectral density parameter is a key parameter that represents the intensity of significant harmonic pure tone, reflecting the sound energy distribution of the pure tone at the corresponding frequency; the significant harmonic pure tone data is a dataset formed by integrating the above two types of parameters.

[0115] This embodiment identifies significant harmonic pure tones that can be effectively perceived by the human ear in high-frequency transformer noise through three steps: spectrum analysis, peak screening, and parameter extraction. This solves the problems of unclear thresholds, low recognition accuracy, and incomplete parameter extraction in traditional pure tone recognition.

[0116] In one exemplary embodiment, the pre-construction process of the audibility-subjective correction value relationship includes:

[0117] S301: Select broadband noise with the same frequency coverage as the high-frequency transformer noise sample as the reference noise sample, and establish a linear correlation between the reference A-weighted sound pressure level of the reference sample and the subjective annoyance score.

[0118] Among them, the reference noise sample is a broadband noise used to construct the benchmark for subjective correction value calculation. Its frequency coverage is consistent with that of the high-frequency transformer noise sample, and its spectrum is uniform, which is used to eliminate the influence of frequency range differences on subjective evaluation; the benchmark A-weighted sound pressure level ( () is the A-weighted sound pressure level of the reference noise sample, reflecting the objective intensity of the reference noise; the linear correlation is " - Subjective annoyance level" linear regression baseline curve.

[0119] For example, several broadband noise samples with uniform spectrum and analysis frequency range consistent with the high-frequency transformer noise samples are selected as reference noise samples. Their sound pressure level coverage is as large as possible to ensure matching of high-frequency transformer noise samples of different intensities. A subjective annoyance score is obtained for each reference sample through a subjective evaluation process. The reference A-weighted sound pressure level of the reference sample is used as the reference score. Using a subjective annoyance score (y) as the dependent variable and a linear regression baseline curve as the independent variable, we construct: y = a × +b (where a and b are regression coefficients), to find the coefficient of determination R of the curve. 2 ≥0.85, ensuring the reliability of the linear relationship, if R 2 If the value is less than 0.85, a new benchmark sample or adjustment of the subjective evaluation data is required until the requirements are met.

[0120] S302: Obtain the measured A-weighted sound pressure level and subjective annoyance score of the high-frequency transformer noise sample, and use the linear correlation to match the benchmark A-weighted sound pressure level corresponding to the subjective annoyance score of the high-frequency transformer noise sample.

[0121] Among them, the measured A-weighted sound pressure level (L Aeq The sound pressure level (SPL) of the high-frequency transformer noise sample is the actual A-weighted SPL, reflecting the objective intensity of the measured noise. The subjective annoyance score is obtained through a subjective evaluation process, reflecting the subjective annoyance perceived by the human ear to the measured noise. The matching process involves matching the subjective annoyance score of the measured sample with the appropriate values. On the linear regression baseline curve of "subjective annoyance level", find the corresponding baseline A-weighted sound pressure level (SPL). ).

[0122] For example, the measured A-weighted sound pressure level (L) of each measured noise sample from a high-frequency transformer is obtained through a noise acquisition process. AeqThe subjective annoyance score of each measured sample is obtained through a subjective evaluation process; the subjective annoyance score of each measured sample is then substituted into the constructed " - Subjective annoyance level" linear regression baseline curve (y=a× +b), and the corresponding reference A-weighted sound pressure level is obtained by reverse calculation ( This ensures that each measured sample can be matched with a unique corresponding sample. .

[0123] S303: The difference between the matched baseline A-weighted sound pressure level and the measured A-weighted sound pressure level is used as the comprehensive subjective correction value for the corresponding sample.

[0124] Among them, the comprehensive subjective correction value (KT) total The noise level is a parameter used to reflect the difference in subjective perception between the measured noise of a high-frequency transformer and the reference broadband noise. Its core logic is the difference in objective sound pressure level between the reference noise and the measured noise under the same subjective annoyance level.

[0125] For example, for each measured noise sample of a high-frequency transformer, the matched reference A-weighted sound pressure level (SPL) is used. ) and the measured A-weighted sound pressure level (L) of the sample Aeq Substitute into the formula The comprehensive subjective correction value (KT) for this sample was calculated. total If the calculation result is positive, it means that under the same subjective annoyance level, the objective sound pressure level of the measured noise is lower than the reference noise, and the objective sound pressure level needs to be increased by the correction value to match the subjective perception; if it is negative, it means that the objective sound pressure level of the measured noise is higher than the reference noise, and the objective sound pressure level needs to be reduced by the correction value to ensure that the correction logic matches the subjective feeling of the human ear.

[0126] S304: Based on the proportion of the acoustic power spectral density of each harmonic in the high-frequency transformer noise sample, the comprehensive subjective correction value is decomposed to obtain the independent subjective correction value corresponding to each harmonic.

[0127] Among them, the proportion of sound power spectral density (ω) i The power spectral density of a single harmonic pure tone is the proportion of the total power spectral density of all harmonic pure tones in the measured sample, used to characterize the contribution of a single harmonic to the overall noise; Independent Subjective Correction Value (KT) i The subjective correction value is the subjective correction value corresponding to each harmonic pure tone after the comprehensive subjective correction value is split according to the proportion of sound power spectral density, and is used to correct the objective sound pressure level of a single pure tone.

[0128] For example, the sound power spectral density of each harmonic in the obtained significant harmonic pure tone data is extracted, and the proportion of the sound power spectral density of each harmonic to the total sound power spectral density of all significant harmonic pure tones in the measured sample is calculated, i.e., the proportion of sound power spectral density (ω).i ), ensuring ω of all harmonics i The sum is 1; the calculated comprehensive subjective correction value (KT) is then used. total Substitute into the splitting formula:

[0129] ;

[0130] KT total The harmonic pure tone is decomposed to obtain an independent subjective correction value (KT) for each harmonic pure tone. i This ensures that the greater the contribution of a harmonic, the higher the weight of its corresponding subjective correction value, thus guaranteeing the rationality of the correction value.

[0131] S305: Divide multiple preset frequency bands according to the preset harmonic frequency range division rules, and perform fitting calculations on the audibility of each harmonic in the same frequency band and the independent subjective correction value to obtain the audibility-subjective correction value relationship of the corresponding frequency band.

[0132] Among them, the preset harmonic frequency range division rule is a frequency band division standard formulated based on the sensitivity of human hearing, used to classify harmonics of different frequencies for easy engineering applications; fitting calculation refers to establishing the harmonic audibility (TA) within the same frequency band through mathematical methods. i ) and independent subjective correction value (KT) i The relationship between audibility and subjective correction value (KT-TA) is used to quickly query the subjective correction value based on audibility.

[0133] For example, based on the preset harmonic frequency range division rules, the high-frequency transformer noise is divided into 5 preset frequency bands: 1-4kHz (the most sensitive frequency band for the human ear), 4-8kHz (the sensitive frequency band for sharpness and harshness), 8-12kHz (the second most sensitive frequency band for the human ear), 12-16kHz (the frequency band with lower human ear sensitivity), and 16-20kHz (the frequency band with no human ear sensitivity). The significant harmonic pure tones of all measured samples are assigned to the corresponding preset frequency bands according to their frequencies. For each frequency band, the audibility (TA) of all harmonics within that band is collected. i and independent subjective correction value KT i This process creates a fitted dataset for the frequency band. The fitted dataset is preprocessed; if the KT value of a data set deviates from ±2 standard deviations from other data at the same frequency, it is directly discarded to avoid affecting the fitting results. A univariate linear regression fitting method is preferred, and the KT-TA fitting formula for the frequency band is constructed: KT = k × TA + b (k and b are fitting coefficients). The coefficient of determination R after fitting is then calculated. 2 If R 2 If R ≥ 0.85, then the fitting formula represents the relationship between audibility and subjective correction value for that frequency band; if R 2 If the value is less than 0.85, the fitting formula is expanded into a quadratic polynomial: KT = k1TA2 +k2×TA+b (k1, k2, b are fitting coefficients), refit until R0 2 ≥0.85; Repeat the above process to obtain the following relationship between audibility and subjective correction values ​​for each of the five preset frequency bands:

[0134] Band 1 (1~4kHz): ;

[0135] Band2 (4~8kHz): ;

[0136] Band3 (8~12kHz): ;

[0137] Band 4 (12~16kHz): ;

[0138] Band 5 (16~20kHz): .

[0139] This embodiment constructs a KT-TA relationship that conforms to human subjective perception and is divided by frequency band by selecting benchmark samples, building linear correlation relationships, calculating and splitting comprehensive correction values, and fitting frequency bands. This solves the problems of difficulty in quantifying subjective correction values ​​and inconsistency in pure tone correction logic across different frequency bands.

[0140] In an exemplary embodiment, the corrected sound pressure levels of all simple tones within the same frequency band are superimposed to obtain the comprehensive corrected sound pressure level for the corresponding frequency band, including:

[0141] S401: According to the preset frequency band division rules, all simple tone corrected sound pressure levels are divided into their respective preset frequency bands.

[0142] Among them, the simple tone corrected sound pressure level (L mod,i () is the result of superimposing the objective sound pressure level of a single harmonic pure tone with its corresponding subjective correction value, i.e. ,in Let be the objective sound pressure level of the i-th harmonic pure tone. The subjective correction value for the i-th harmonic pure tone; the preset frequency band division rule is a number of frequency band division standards formulated in combination with the human ear's hearing sensitivity; the division process is to correct the sound pressure level of each pure tone and classify it into the corresponding preset frequency band according to the frequency of its corresponding harmonic.

[0143] For example, first determine the preset frequency band division rules: 1-4kHz, 4-8kHz, 8-12kHz, 12-16kHz, 16-20kHz; extract the corrected sound pressure level (L) for each simple tone. mod,i The frequency parameters of the corresponding harmonics; based on the frequency parameters, each L...mod,i It is assigned to the corresponding preset frequency band, for example, the L corresponding to the harmonic with a frequency of 3kHz. mod,i Dividing the frequency band into 1-4kHz, the L corresponding to the 6kHz harmonic. mod,i It is allocated to the 4-8kHz frequency band.

[0144] S402: Perform sound pressure level energy conversion processing on all simple tone corrected sound pressure levels within the same preset frequency band in sequence, and accumulate and integrate all converted energy data under the same frequency band to obtain the frequency band energy.

[0145] Among them, the sound pressure level energy conversion processing is to modify the sound pressure level of a simple tone (L). mod,i The process of converting sound pressure levels into corresponding energy values ​​follows the principle of acoustic energy superposition. The frequency band energy is the sum of the energy values ​​corresponding to the corrected sound pressure levels of all pure tones within the same preset frequency band, reflecting the comprehensive energy level of all pure tones within that frequency band.

[0146] For example, for each preset frequency band, all simple tone corrected sound pressure levels (L) within that frequency band are collected. mod,i ); for each L mod,i The sound pressure level energy conversion process is performed sequentially, using the formula 10^0.1L. mod,i The sound pressure level unit is converted to an energy unit; all converted energy data within the frequency band are accumulated and integrated to obtain the frequency band energy, i.e. .

[0147] S403: Perform logarithmic conversion on the frequency band energy to obtain the comprehensive corrected sound pressure level for the corresponding preset frequency band.

[0148] The logarithmic conversion process converts the frequency band energy into a comprehensive corrected sound pressure level (X). m The process of conversion follows the acoustic calculation rules for sound pressure level; the comprehensive correction of sound pressure level (X) conforms to the calculation standards for sound pressure level in acoustics. m The sound pressure level is the combined corrected sound pressure level of all pure tones within the same frequency band, reflecting the overall contribution of pure tone noise in that frequency band to the overall annoyance level.

[0149] For example, the frequency band energy of each preset frequency band is substituted into the following logarithmic conversion formula:

[0150] ;

[0151] The logarithmic operation yields the overall corrected sound pressure level (X) for the preset frequency band. m Repeat the above process to calculate the comprehensive corrected sound pressure level (X1-X5) for each of the five preset frequency bands, ensuring that the calculation results are accurate and conform to acoustic calculation specifications.

[0152] This embodiment integrates the corrected sound pressure level of a single pure tone into a frequency band comprehensive corrected sound pressure level through three steps: frequency band division, energy conversion, and logarithmic conversion. This solves the problem of difficulty in quantifying the comprehensive contribution of frequency bands when multiple pure tones are superimposed.

[0153] In one exemplary embodiment, the pre-construction process of a multiple regression model based on a modified sound pressure level-annoyance subjective rating includes:

[0154] S501: Divide all high-frequency transformer noise samples into independent training sample groups and validation sample groups.

[0155] The training sample set is a set of samples used to build and fit the multivariate regression model. Its data is used to determine the model's coefficients (perceived weights for each frequency band and constant intercepts). The validation sample set is a set of samples used to verify the model's goodness of fit. Its data does not participate in model fitting and is only used to verify the model's prediction accuracy. The two sets of samples are independent of each other to ensure the objectivity and reliability of the validation results.

[0156] For example, collect all high-frequency transformer noise samples (no less than 100, covering all operating frequencies and voltage levels supported by the high-frequency transformer), and use random sampling to divide 70% of the samples into a training sample group for subsequent model construction and fitting; divide the remaining 30% of the samples into a validation sample group for verifying the goodness of fit of the model; ensure that the two groups of samples are consistent in distribution in terms of operating conditions, frequency, voltage level, etc., are independent of each other, and have no overlap, so as to avoid deviation in the validation results.

[0157] S502: Obtain the comprehensive corrected sound pressure level of each noise sample in the training sample group under all preset frequency bands. Use the comprehensive corrected sound pressure level corresponding to each preset frequency band as the independent variable and the subjective annoyance score of each noise sample as the dependent variable to construct and fit a multiple regression model.

[0158] Among them, the multiple regression model is a mathematical model used to predict the overall annoyance level of pure tone noise from high-frequency transformers. Its independent variable is the comprehensive corrected sound pressure level (X1-X5) of five preset frequency bands, and the dependent variable is the subjective annoyance score of the noise sample. The fitting process is a process of determining the perceived weight (c1-c5) and constant intercept (d) of each frequency band in the model through mathematical methods to ensure that the model can accurately reflect the correlation between the comprehensive corrected sound pressure level and the subjective annoyance score.

[0159] For example, for each noise sample in the training sample group, calculate its comprehensive corrected sound pressure level (X1-X5) in 5 preset frequency bands; collect the subjective annoyance score for each training sample; and construct a multiple regression model with the comprehensive corrected sound pressure level (X1-X5) in 5 preset frequency bands as independent variables and the subjective annoyance score (y) of each sample as the dependent variable:

[0160] ;

[0161] Where c1-c5 are the sensing weights for each frequency band, and d is the constant intercept. To predict subjective annoyance scores, statistical methods are used to fit the model, and the specific values ​​of c1-c5 and d are calculated to ensure that the fitting process closely matches the training sample data.

[0162] S503: Validate the multiple regression model using the validation sample group. Once the validation is successful, the final multiple regression model is obtained.

[0163] Model validation is the process of verifying the prediction accuracy of a multiple regression model by validating the data of the sample group. The standard for successful validation is the coefficient of determination R between the model's predicted value and the actual subjective annoyance scores of the validation samples. 2 A value of ≥0.85 is used to ensure that the model can accurately predict the overall annoyance level of pure-tone noise from high-frequency transformers; the final multiple regression model is a validated model that can be used to predict actual annoyance levels.

[0164] For example, for each noise sample within the validation sample group, the L of each harmonic is calculated. Aeqi TA i L mod,i The system calculates the combined corrected sound pressure level (X1-X5) for five preset frequency bands; substitutes X1-X5 for each validation sample into the fitted multiple regression model to calculate the predicted subjective annoyance score for that sample; collects the actual subjective annoyance scores for each validation sample and calculates the coefficient of determination R between the predicted and actual values. 2 If R 2 A value ≥0.85 indicates that the model fits well, and this model is the final multiple regression model; if R... 2 If the value is less than 0.85, it indicates that the model's prediction accuracy is insufficient. Return to the KT-TA relationship fitting process, check and remove outliers in the fitted data, refit the KT-TA relationship, and repeat the above process until the model is validated and the final multiple regression model is obtained.

[0165] This embodiment constructs a multiple regression model that can accurately predict the overall annoyance level of pure tone noise from high-frequency transformers through three core steps: sample division, model fitting, and model validation. This solves the problems of difficulty in quantifying subjective feelings and low prediction accuracy in traditional annoyance level evaluation.

[0166] In one exemplary embodiment, the process for determining the subjective annoyance score includes:

[0167] S601: Select evaluation subjects that meet the hearing requirements and set a unified scoring metric.

[0168] Among them, the evaluation subject is the person who participates in the subjective evaluation and scores the annoyance level of the noise sample. Their hearing condition directly affects the reliability of the subjective evaluation results. The scoring metric is a standard used to unify the scoring scale of the evaluation subjects, ensure that the scoring logic of all evaluation subjects is consistent, and avoid excessive differences in individual scores.

[0169] For example, participants with normal hearing were selected, with a male-to-female ratio of 1:1 and a minimum of 30 participants. All participants underwent professional training and were familiar with the evaluation process and scoring criteria. A unified scoring metric was established using a 7-level annoyance scale: 1 = No annoyance at all; 2 = Mild annoyance; 3 = Some annoyance; 4 = Moderate annoyance; 5 = Severe annoyance; 6 = Severe annoyance; 7 = Extremely severe annoyance. Since this experiment involves human subjects, ethical review was completed according to standard procedures and approval was obtained from relevant departments before the experiment began to ensure compliance. Before the evaluation, the purpose of the experiment and the meaning of the scale were explained to all participants, and three demonstration samples of different TA levels (low / medium / high TA) were played to ensure that all participants had a consistent understanding of the scoring criteria and to avoid individual misunderstandings affecting the scoring results.

[0170] S602: Under a fixed acoustic test environment, high-frequency transformer noise samples and reference noise samples are played in sequence. The evaluation body evaluates each sample according to the scoring metric standard to obtain the initial score data for each noise sample.

[0171] The fixed acoustic test environment refers to an acoustic environment with no reflection and low background noise (semi-anechoic chamber) to ensure that the playback noise is not disturbed by the outside world and to ensure that the noise heard by the evaluation subject is highly consistent with the original collected data; the initial score data is the original score obtained by the evaluation subject after rating each noise sample.

[0172] For example, the fixed acoustic test environment is a semi-anechoic chamber, which provides a reflection-free, low-background-noise acoustic environment (background noise level below 20 dB(A)) to ensure that the playback noise is not disturbed by external factors. The evaluation system hardware consists of a workstation, an acoustic power amplifier, and high-fidelity headphones. The acoustic power amplifier amplifies the collected high-frequency transformer noise samples (no fewer than 100, covering all operating frequencies and voltage levels) and reference noise samples (20 samples), and then the high-fidelity headphones play back the audio to the evaluation subject in both ears. The high-fidelity headphones have wide bandwidth and high resolution. The high-resolution acoustic characteristics accurately reproduce the detailed features and dynamic range of noise, allowing the evaluator to obtain a noise listening experience close to actual working conditions. Each noise sample is played for 5 seconds, and the playback order is randomized to avoid the evaluator developing scoring inertia. After every 20 samples, the evaluator is allowed a 10-minute break to avoid auditory fatigue. The evaluator uses a 7-level annoyance scale and their own subjective auditory perception to rate each played noise sample. The evaluation system automatically records the evaluator's number, sample number, and score, forming the initial score data for each noise sample.

[0173] S603: Perform correlation statistical analysis on the initial scoring data, remove abnormal scoring data that deviates from the overall evaluation pattern, and use the remaining scoring data as valid scoring data.

[0174] Among them, correlation statistical analysis is a statistical method used to test the consistency of ratings among evaluation subjects. Its core is to calculate the correlation coefficient between ratings of each evaluation subject and to judge the reliability of the rating data. Abnormal rating data refers to data that deviates from the overall evaluation pattern and differs too much from the ratings of other evaluation subjects. Such data will affect the accuracy of subjective annoyance ratings and should be removed. Valid rating data is rating data that, after screening, can reflect the overall evaluation pattern and has high reliability.

[0175] For example, initial rating data for each noise sample from all evaluation subjects are collected. Spearman correlation analysis is used to calculate the rating correlation coefficient between each evaluation subject. For each evaluation subject, the rating correlation coefficient between it and all other evaluation subjects is calculated. Rating data from evaluation subjects with an average correlation coefficient higher than 0.7 are selected, and all rating data from evaluation subjects with an average correlation coefficient lower than 0.7 are identified as abnormal rating data and removed. After removing abnormal data, the remaining rating data are the valid rating data.

[0176] S604: After normalizing the valid scoring data, the subjective annoyance score corresponding to each noise sample is obtained.

[0177] Normalization is a process of scaling and integrating valid rating data according to a unified standard. Its core is to eliminate subtle differences in the rating scales of different evaluators, so that the final subjective annoyance score has uniformity and comparability. The subjective annoyance score is a quantitative value that can comprehensively reflect the subjective feelings of all valid evaluators after normalization.

[0178] For example, for each noise sample, all valid rating data are collected; the valid rating data are normalized using the arithmetic mean method, and the arithmetic mean of all valid ratings is calculated; after the calculation, the average is verified, and if there are individual valid ratings that deviate from the average by ±1.5 standard deviations, the deviating ratings are further removed, and the arithmetic mean of the remaining valid ratings is recalculated; the final average is the subjective annoyance rating corresponding to the noise sample. The rating result is retained to one decimal place to ensure the accuracy and comparability of the rating. It can be directly used as the dependent variable of a multiple regression model for model fitting, or as a direct evaluation result of the annoyance of high-frequency transformer pure tone noise.

[0179] This embodiment constructs a standardized and reusable subjective evaluation system, which solves the problems of large individual differences in subjective annoyance scores, non-standard evaluation process, and insufficient reliability of results.

[0180] Compared with the prior art, the present invention has the following advantages:

[0181] 1. This invention overcomes the limitation of traditional A-weighted sound pressure level (SPL) being only applicable to broadband noise. Referring to ISO / TS20065:2022 and establishing a frequency-band KT-TA subjective correction relationship through standardized psychoacoustic experiments, it correlates the objective physical indicators of pure tones with the subjective level of annoyance experienced by the human body. Simultaneously, through… The decomposition method enables individual correction of multi-harmonic pure tones, accurately quantifies the additional subjective disturbance of high-frequency multi-harmonic pure tones compared to broadband noise, and avoids the underestimation of pure tone disturbance by traditional evaluation methods.

[0182] 2. This invention divides 1kHz-20kHz into 5 fixed frequency bands and designs energy superposition rules for multiple pure tones in the same frequency band. Frequency bands with no harmonic contribution are directly set to 0. It can be adapted to high-frequency transformers with different fundamental frequencies, different voltage levels, and different load conditions. Regardless of which frequency bands the harmonics are distributed in, they can be modeled and evaluated through a unified 5-dimensional feature. There is no need to redesign and modify the rules or model for different transformers, which effectively solves the problem of poor universality of existing technologies.

[0183] 3. This invention standardizes the design of subjective evaluation experiments, ensuring the reliability of subjective scores by screening evaluation subjects and removing outliers through data preprocessing; it constructs a regression curve of benchmark broadband noise to determine the comprehensive correction value, providing an objective basis for the establishment of subjective correction rules; the model construction adopts a 7:3 division of training and validation sets to ensure that the model can accurately reflect the contribution of harmonics in each frequency band to the overall annoyance level, and the prediction results are consistent with actual human perception.

[0184] 4. The evaluation process of this invention is logically complete. From equipment calibration, measurement point planning, noise acquisition to pure tone recognition, audibility calculation, model construction and verification, each step has clear operating specifications, input and output parameters and calculation rules. All calculations can be achieved through conventional acoustic equipment and general data processing software without the need for complex algorithms or special equipment. At the same time, the supporting evaluation system adopts a modular design and can be directly integrated into the existing noise detection platform, which facilitates rapid detection and evaluation on the engineering site.

[0185] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0186] Based on the same inventive concept, this application also provides a high-frequency transformer pure-tone noise annoyance evaluation device for implementing the aforementioned high-frequency transformer pure-tone noise annoyance evaluation method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in the embodiments of the high-frequency transformer pure-tone noise annoyance evaluation device provided below can be found in the limitations of the high-frequency transformer pure-tone noise annoyance evaluation method described above, and will not be repeated here.

[0187] Please see Figure 3 This invention also provides a device for evaluating the annoyance level of pure-tone noise in high-frequency transformers, comprising:

[0188] The noise acquisition module is used to collect noise data of high-frequency transformers under different operating conditions to obtain noise acquisition data;

[0189] The pure tone recognition module is used to perform peak detection on noise acquisition data, identify all significant harmonic pure tones that meet the preset peak determination conditions, and obtain significant harmonic pure tone data.

[0190] The pure tone audibility calculation module is used to perform audibility quantification calculation on each significant harmonic pure tone based on significant harmonic pure tone data, and obtain the audibility of each significant harmonic pure tone;

[0191] The subjective correction value determination module is used to determine the subjective correction value of each significant harmonic pure tone based on audibility from a pre-constructed audibility-subjective correction value relationship. The audibility-subjective correction value relationship is constructed separately for multiple preset frequency bands and is used to represent the relationship between the audibility of each harmonic in the corresponding frequency band and the corresponding subjective correction value. The subjective correction value is the difference between the A-weighted sound pressure level of the reference noise sample and the A-weighted sound pressure level of the measured high-frequency transformer noise sample under the same subjective evaluation conditions.

[0192] The modified sound pressure level calculation module is used to add the A-weighted sound pressure level of each significant harmonic pure tone in each preset frequency band to the corresponding subjective correction value to obtain the modified sound pressure level of a simple tone, and to superimpose all the modified sound pressure levels of simple tones in the same frequency band to obtain the comprehensive modified sound pressure level of the corresponding frequency band.

[0193] The annoyance assessment module is used to obtain the corresponding subjective score based on the comprehensive corrected sound pressure level using a pre-constructed multiple regression model based on the corrected sound pressure level and the annoyance subjective score. The multiple regression model is a multiple regression model fitted with the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable.

[0194] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0195] Reference Figure 3This invention also provides a computer device, including: a memory and a processor, and a computer program stored in the memory. When the computer program is executed on the processor, it performs the following steps:

[0196] Noise data of high-frequency transformers under different operating conditions was collected to obtain noise acquisition data;

[0197] Peak detection is performed on the noise acquisition data to identify all significant harmonic pure tones that meet the preset peak determination conditions, and significant harmonic pure tone data is obtained.

[0198] Based on the significant harmonic pure tone data, the audibility of each significant harmonic pure tone is quantitatively calculated to obtain the audibility of each significant harmonic pure tone.

[0199] Based on audibility, the subjective correction value of each significant harmonic pure tone is determined from the pre-constructed audibility-subjective correction value relationship; the audibility-subjective correction value relationship is constructed separately for multiple preset frequency bands and is used to represent the relationship between the audibility of each harmonic in the corresponding frequency band and the corresponding subjective correction value; the subjective correction value is the difference between the A-weighted sound pressure level of the reference noise sample and the measured A-weighted sound pressure level of the high-frequency transformer noise sample under the same subjective evaluation conditions;

[0200] For each preset frequency band, the A-weighted sound pressure level of each significant harmonic pure tone in the frequency band is added to the corresponding subjective correction value to obtain the simple tone corrected sound pressure level. All simple tone corrected sound pressure levels in the same frequency band are superimposed to obtain the comprehensive corrected sound pressure level of the corresponding frequency band.

[0201] Based on the comprehensive corrected sound pressure level, the corresponding subjective score is obtained using a pre-constructed multiple regression model based on the corrected sound pressure level and the subjective annoyance score. The multiple regression model is a multiple regression model fitted with the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable.

[0202] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 3 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. They may include more or fewer components than shown in the illustration, or combinations of certain components, or different components. For example, they may also include input / output devices, network access devices, etc.

[0203] The processor referred to can be a Central Processing Unit (CPU), but it can also 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. A general-purpose processor can be a microprocessor or any conventional processor.

[0204] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, boot loader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0205] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the following steps:

[0206] Noise data of high-frequency transformers under different operating conditions was collected to obtain noise acquisition data;

[0207] Peak detection is performed on the noise acquisition data to identify all significant harmonic pure tones that meet the preset peak determination conditions, and significant harmonic pure tone data is obtained.

[0208] Based on the significant harmonic pure tone data, the audibility of each significant harmonic pure tone is quantitatively calculated to obtain the audibility of each significant harmonic pure tone.

[0209] Based on audibility, the subjective correction value of each significant harmonic pure tone is determined from the pre-constructed audibility-subjective correction value relationship; the audibility-subjective correction value relationship is constructed separately for multiple preset frequency bands and is used to represent the relationship between the audibility of each harmonic in the corresponding frequency band and the corresponding subjective correction value; the subjective correction value is the difference between the A-weighted sound pressure level of the reference noise sample and the measured A-weighted sound pressure level of the high-frequency transformer noise sample under the same subjective evaluation conditions;

[0210] For each preset frequency band, the A-weighted sound pressure level of each significant harmonic pure tone in the frequency band is added to the corresponding subjective correction value to obtain the simple tone corrected sound pressure level. All simple tone corrected sound pressure levels in the same frequency band are superimposed to obtain the comprehensive corrected sound pressure level of the corresponding frequency band.

[0211] Based on the comprehensive corrected sound pressure level, the corresponding subjective score is obtained using a pre-constructed multiple regression model based on the corrected sound pressure level and the subjective annoyance score. The multiple regression model is a multiple regression model fitted with the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable.

[0212] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0213] This invention provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0214] Noise data of high-frequency transformers under different operating conditions was collected to obtain noise acquisition data;

[0215] Peak detection is performed on the noise acquisition data to identify all significant harmonic pure tones that meet the preset peak determination conditions, and significant harmonic pure tone data is obtained.

[0216] Based on the significant harmonic pure tone data, the audibility of each significant harmonic pure tone is quantitatively calculated to obtain the audibility of each significant harmonic pure tone.

[0217] Based on audibility, the subjective correction value of each significant harmonic pure tone is determined from the pre-constructed audibility-subjective correction value relationship; the audibility-subjective correction value relationship is constructed separately for multiple preset frequency bands and is used to represent the relationship between the audibility of each harmonic in the corresponding frequency band and the corresponding subjective correction value; the subjective correction value is the difference between the A-weighted sound pressure level of the reference noise sample and the measured A-weighted sound pressure level of the high-frequency transformer noise sample under the same subjective evaluation conditions;

[0218] For each preset frequency band, the A-weighted sound pressure level of each significant harmonic pure tone in the frequency band is added to the corresponding subjective correction value to obtain the simple tone corrected sound pressure level. All simple tone corrected sound pressure levels in the same frequency band are superimposed to obtain the comprehensive corrected sound pressure level of the corresponding frequency band.

[0219] Based on the comprehensive corrected sound pressure level, the corresponding subjective score is obtained using a pre-constructed multiple regression model based on the corrected sound pressure level and the subjective annoyance score. The multiple regression model is a multiple regression model fitted with the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable.

[0220] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0221] 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.

[0222] In the embodiments disclosed in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or 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 displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0223] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the annoyance level of pure-tone noise in a high-frequency transformer, characterized in that, Includes the following steps: Noise data of high-frequency transformers under different operating conditions was collected to obtain noise acquisition data; Peak detection is performed on the noise acquisition data to identify all significant harmonic pure tones that meet the preset peak determination conditions, and significant harmonic pure tone data is obtained. Based on the significant harmonic pure tone data, the audibility of each significant harmonic pure tone is quantitatively calculated to obtain the audibility of each significant harmonic pure tone. Based on the audibility, the subjective correction value of each significant harmonic pure tone is determined from the pre-constructed audibility-subjective correction value relationship; the audibility-subjective correction value relationship is constructed separately according to multiple preset frequency bands, and is used to represent the relationship between the audibility of each harmonic in the corresponding frequency band and the corresponding subjective correction value; the subjective correction value is the difference between the A-weighted sound pressure level of the reference noise sample and the measured A-weighted sound pressure level of the high-frequency transformer noise sample under the same subjective evaluation conditions; For each preset frequency band, the A-weighted sound pressure level of each significant harmonic pure tone in the frequency band is added to the corresponding subjective correction value to obtain the simple tone corrected sound pressure level, and all the simple tone corrected sound pressure levels in the same frequency band are superimposed to obtain the comprehensive corrected sound pressure level of the corresponding frequency band. Based on the comprehensive corrected sound pressure level, the corresponding subjective score is obtained using a pre-constructed multiple regression model based on the corrected sound pressure level and the subjective annoyance score. The multiple regression model is a multiple regression model fitted with the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable.

2. The high-frequency transformer pure tone noise annoyance evaluation method according to claim 1, characterized by, Peak detection is performed on the noise acquisition data to identify all significant harmonic pure tones that meet the preset peak determination criteria, and significant harmonic pure tone data is obtained, including: The noise acquisition data is subjected to spectrum analysis using a peak frequency detection method to obtain the spectrum analysis results. The spectrum analysis results are filtered according to the preset peak determination conditions, and acoustic signals whose peak signals meet the threshold difference requirements are extracted, and the acoustic signals are used as the significant harmonic pure tones; The frequency parameters and sound pressure power spectral density parameters of each significant harmonic pure tone are extracted and integrated to obtain the significant harmonic pure tone data.

3. The high-frequency transformer pure tone noise annoyance evaluation method according to claim 1, characterized by, The pre-construction process for the audibility-subjective correction value relationship includes: Broadband noise with the same frequency coverage as the high-frequency transformer noise sample was selected as the reference noise sample, and a linear correlation was established between the reference A-weighted sound pressure level of the reference sample and the subjective annoyance score. The measured A-weighted sound pressure level and subjective annoyance score of the high-frequency transformer noise sample are obtained, and the baseline A-weighted sound pressure level corresponding to the subjective annoyance score of the high-frequency transformer noise sample is matched using the linear correlation. The difference between the matched baseline A-weighted sound pressure level and the measured A-weighted sound pressure level is used as the comprehensive subjective correction value for the corresponding sample. Based on the proportion of the acoustic power spectral density of each harmonic in the high-frequency transformer noise sample, the comprehensive subjective correction value is decomposed to obtain the independent subjective correction value corresponding to each harmonic. Multiple preset frequency bands are divided according to the preset harmonic frequency range division rules, and the audibility of each harmonic in the same frequency band is fitted with the independent subjective correction value to obtain the audibility-subjective correction value relationship of the corresponding frequency band.

4. The high-frequency transformer pure tone noise annoyance evaluation method according to claim 1, characterized by, The overall corrected sound pressure level of the corresponding frequency band is obtained by superimposing all the simple tone corrected sound pressure levels within the same frequency band, including: According to the preset frequency band division rules, all the simple tone corrected sound pressure levels are divided into their respective preset frequency bands; The sound pressure level energy conversion process is performed sequentially on all the simple tone corrected sound pressure levels within the same preset frequency band, and the converted energy data of all the same frequency band are accumulated and integrated to obtain the frequency band energy. Logarithmic conversion is performed on the energy of the frequency band to obtain the comprehensive corrected sound pressure level of the corresponding preset frequency band.

5. The high-frequency transformer pure tone noise annoyance evaluation method according to claim 1, characterized by, The pre-construction process of the multiple regression model based on modified sound pressure level-annoyance subjective score includes: All high-frequency transformer noise samples were divided into independent training sample groups and validation sample groups. The comprehensive corrected sound pressure level of each noise sample in the training sample group is obtained in all preset frequency bands. The comprehensive corrected sound pressure level corresponding to each preset frequency band is used as an independent variable, and the subjective annoyance score of each noise sample is used as a dependent variable. A multiple regression model is constructed and fitted. The multiple regression model is validated using the validation sample set, and the final multiple regression model is obtained after the validation is successful.

6. The high-frequency transformer pure tone noise annoyance degree evaluation method according to claim 3 or 5, characterized by, The process for determining the subjective annoyance score includes: Select evaluation subjects that meet the hearing requirements and set a unified scoring metric. Under a fixed acoustic test environment, high-frequency transformer noise samples and reference noise samples are played in sequence. The evaluation subject evaluates each sample according to the scoring metric standard to obtain the initial score data for each noise sample. The initial scoring data is subjected to correlation statistical analysis to remove abnormal scoring data that deviates from the overall evaluation pattern, and the remaining scoring data is taken as valid scoring data. After normalizing the valid scoring data, the subjective annoyance score corresponding to each noise sample is obtained.

7. A high-frequency transformer pure tone noise annoyance degree evaluation device characterized by comprising: include: The noise acquisition module is used to collect noise data of high-frequency transformers under different operating conditions to obtain noise acquisition data; The pure tone recognition module is used to perform peak detection on the noise acquisition data, identify all significant harmonic pure tones that meet the preset peak determination conditions, and obtain significant harmonic pure tone data. The pure tone audibility calculation module is used to perform audibility quantification calculation on each of the significant harmonic pure tones based on the significant harmonic pure tone data, and obtain the audibility of each significant harmonic pure tone; The subjective correction value determination module is used to determine the subjective correction value of each significant harmonic pure tone from a pre-constructed audibility-subjective correction value relationship based on the audibility; the audibility-subjective correction value relationship is constructed separately according to multiple preset frequency bands and is used to represent the relationship between the audibility of each harmonic in the corresponding frequency band and the corresponding subjective correction value; the subjective correction value is the difference between the A-weighted sound pressure level of the reference noise sample and the A-weighted sound pressure level of the measured high-frequency transformer noise sample under the same subjective evaluation conditions; The modified sound pressure level calculation module is used to add the A-weighted sound pressure level of each significant harmonic pure tone in each preset frequency band to the corresponding subjective correction value to obtain the modified sound pressure level of a simple tone, and to superimpose all the modified sound pressure levels of the simple tone in the same frequency band to obtain the comprehensive modified sound pressure level of the corresponding frequency band. The annoyance evaluation module is used to obtain the corresponding subjective score based on the comprehensive corrected sound pressure level using a pre-constructed multiple regression model based on the corrected sound pressure level and the annoyance subjective score. The multiple regression model is a multiple regression model fitted with the comprehensive corrected sound pressure level of each preset frequency band as the independent variable and the subjective annoyance score corresponding to the high-frequency transformer noise sample as the dependent variable.

8. A computer device, comprising: The device includes a processor and a memory: The memory is used to store computer programs and send the instructions of the computer programs to the processor; The processor executes, according to the instructions of the computer program, a method for evaluating the annoyance of pure-tone noise in a high-frequency transformer as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements a method for evaluating the annoyance level of pure-tone noise in a high-frequency transformer as described in any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements a method for evaluating the annoyance of pure-tone noise in a high-frequency transformer as described in any one of claims 1-6.

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