A Noise Control System and Method for Suction and Exhaust Based on SVPWM Space Vector Control

Abstract

The application discloses a suction and exhaust noise regulation system and method based on SVPWM space vector control, and relates to the field of noise regulation.The application controls low-frequency noise by fusing mechanical damping, fluid optimization and power electronic control technology; effectively suppresses structural vibration noise by mechanical component stability detection and damping gasket reinforcement, and avoids resonance deterioration problems caused by traditional single noise reduction mode; a high-frequency noise mapping model based on pipeline parameters and air volume data can predict pneumatic noise risks in advance and realize preventive regulation; SVPWM is adopted to break the resonance condition of motor torque pulsation and pneumatic noise by randomizing the switching frequency and vector action time redistribution, and to reduce the high-frequency resonance area ratio to a preset threshold; wherein, by collecting multi-air-volume gear noise data in real time, combining the feedback closed loop formed by the future time prediction set, it is ensured that the noise is always lower than the threshold in long-term use, and performance degradation caused by traditional fixed parameters is avoided.

Description

Technical Field

[0001] This invention belongs to the field of noise control, and more specifically, relates to a noise control system and method for intake and exhaust based on SVPWM space vector control. Background Technology

[0002] Traditional methods struggle to distinguish between low-frequency mechanical vibrations (such as motor vibration) and high-frequency aerodynamic noise (such as duct turbulence), resulting in insufficiently targeted noise reduction measures. Existing technologies lack quantitative models of the relationship between parameters such as pipe curvature and diameter and high-frequency noise, hindering efficient improvements to address design flaws. Furthermore, traditional fixed-frequency SVPWM modulation easily induces resonance between 6K±n harmonics and acoustic modal frequencies, leading to significant intake and exhaust noise. Summary of the Invention

[0003] To address the problems in related technologies, this invention proposes a noise control system and method for intake and exhaust based on SVPWM space vector control, in order to overcome the aforementioned technical problems existing in the prior art.

[0004] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0005] This invention relates to a method for controlling intake and exhaust noise based on SVPWM space vector control, comprising the following steps:

[0006] S1. Collect data on suction and exhaust noise at multiple airflow levels and low frequencies during the operation of the range hood to be controlled; collect duct data from multiple existing range hoods to construct the final high-frequency suction and exhaust noise mapping model.

[0007] S2. If there is low-frequency suction and exhaust noise data greater than or equal to the corresponding threshold in the suction and exhaust noise data collected in S1 and the corresponding future suction and exhaust noise data, proceed to S3; otherwise, treat the range hood to be adjusted as the range hood after low-frequency adjustment and execute S4.

[0008] S3. Reinforce and dampen the various mechanical components of the range hood to be adjusted until the real-time low-frequency suction and exhaust noise data and the corresponding future suction and exhaust noise data both meet the corresponding thresholds, and obtain the range hood after low-frequency adjustment and execute S4.

[0009] S4. Collect suction and exhaust noise data at multiple airflow levels and high frequencies during the operation of the range hood after low-frequency adjustment; if there is high-frequency suction and exhaust noise data greater than or equal to the corresponding threshold in the suction and exhaust noise data collected in S4 and the corresponding future suction and exhaust noise data, execute S5; otherwise, use the range hood after low-frequency adjustment as the final range hood.

[0010] S5. Adjust the duct parameters of the range hood after low-frequency adjustment by a preset number of repetitions in conjunction with the final high-frequency suction and exhaust noise mapping model, and input them into the final high-frequency suction and exhaust noise mapping model for mapping; if the mapping result meets the corresponding threshold within the preset number of repetitions, the final range hood is obtained; otherwise, the range hood after macro-control is obtained.

[0011] S6. Adjust the current random frequency ratio and SVPWM vector action time allocation ratio of the range hood after macro-control until the proportion of high-risk resonance zone and the number of high-frequency noise meet the conditions.

[0012] Preferably, step S1 includes the following steps:

[0013] S11. Set the sampling frequency, the range hood to be adjusted, and the corresponding airflow level type to obtain the current airflow level type set; install and run the range hood to be adjusted.

[0014] S12. After the range hood to be controlled is installed and put into operation, collect the suction and exhaust noise data of each air volume level and low frequency during the operation of the range hood to be controlled according to the sampling frequency and the current air volume level type set, and obtain the real-time low frequency suction and exhaust noise dataset.

[0015] S13. Based on the current set of airflow level types, set different airflow levels and suction and exhaust noise thresholds at high and low frequencies to obtain a set of high-frequency suction and exhaust noise thresholds and a set of low-frequency suction and exhaust noise thresholds.

[0016] S14. In conjunction with the high-frequency suction and exhaust noise threshold set, collect the existing range hoods' pipe bending curvature data, pipe diameter data, and average pipe air volume data for those whose average high-frequency suction and exhaust noise data are less than or greater than or equal to the corresponding high-frequency suction and exhaust noise threshold.

[0017] S15. Based on the pipe bending radius data, pipe diameter data, average pipe air volume data, and corresponding average high-frequency suction and exhaust noise data collected in S14, construct the final high-frequency suction and exhaust noise mapping model.

[0018] By acquiring data across frequency bands, precise location and classification of noise sources are achieved, providing data support for targeted noise reduction. Combined with continuous sampling at multiple speeds, the noise characteristics of the range hood under different operating conditions are comprehensively covered. Pre-calibration using a sound calibrator and recording based on standard sound sources ensure data comparability and laboratory-level accuracy. Equal-interval sampling combined with timestamp recording provides corresponding time-series data for predicting future exhaust noise. Differentiated setting of high-frequency / low-frequency noise thresholds, combined with real-time data monitoring and future noise trend prediction, upgrades from passive response to active intervention. By establishing a coupling relationship model between pipe geometry parameters and fluid characteristics, the generation mechanism of high-frequency howling noise is quantified into a calculable fluid dynamics index for the first time. Compared to traditional single-threshold alarm methods, it can identify abrupt changes in turbulent noise caused by specific combinations of curvature and critical diameter. The high-frequency exhaust noise mapping model provides a three-dimensional optimization space for range hood pipe design: identifying curved transition sections can reduce eddy noise.

[0019] Preferably, step S15 includes the following steps:

[0020] S151. Construct an initial high-frequency absorption and exhaust noise mapping model;

[0021] S152. The initial high-frequency suction and exhaust noise mapping model is trained and tested using the pipe bending radius data, pipe diameter data, average pipe air volume data, and corresponding average high-frequency suction and exhaust noise data collected in S14; after training and testing, the final high-frequency suction and exhaust noise mapping model is obtained.

[0022] The initial high-frequency suction and discharge noise mapping model constructed in this scheme has the ability to extract deep features of the strong nonlinear relationship between pipeline geometric parameters and fluid noise; and can improve feature reuse efficiency when the amount of industrial data is limited; and can process structured parameters and spatial features simultaneously.

[0023] Preferably, step S2 includes the following steps:

[0024] S21. When there is a real-time low-frequency suction and exhaust noise data in the real-time low-frequency suction and exhaust noise data that is greater than or equal to the low-frequency suction and exhaust noise threshold of the corresponding airflow level in the low-frequency suction and exhaust noise threshold set, proceed to S3.

[0025] Otherwise, based on the real-time low-frequency suction and discharge noise dataset, the low-frequency suction and discharge noise data at multiple future times are predicted to obtain the future low-frequency suction and discharge noise dataset.

[0026] S22. Adjust the range hood to be controlled according to the future low-frequency suction and exhaust noise dataset;

[0027] When current or predicted noise exceeds a threshold, automatic regulation is triggered to avoid noise exceeding the limit due to response delay in traditional methods; low-frequency mechanical vibration is handled by implementing customized noise reduction strategies for low-frequency noise characteristics to improve overall noise reduction efficiency.

[0028] Preferably, step S22 includes the following steps:

[0029] S221. When there is a low-frequency suction and exhaust noise data in the future low-frequency suction and exhaust noise data that is greater than or equal to the low-frequency suction and exhaust noise threshold of the corresponding airflow level in the low-frequency suction and exhaust noise threshold set, proceed to S3; otherwise, there is no need to adjust the range hood to be adjusted, and the range hood to be adjusted is used as the range hood after low-frequency adjustment and S4 is executed.

[0030] Preferably, step S3 includes the following steps:

[0031] S31. Set several types of mechanical components for the installation of range hoods to obtain a set of mechanical component types to be controlled; and, in conjunction with the set of mechanical component types to be controlled, perform stability testing and reinforcement on each mechanical component of the range hood to be controlled.

[0032] S32. After stability testing and reinforcement are completed, the range hood after initial low-frequency adjustment is obtained;

[0033] If, after the initial low-frequency adjustment, there are still low-frequency suction and exhaust noise data at each airflow level and at the low frequency, as well as the corresponding future suction and exhaust noise data, that are greater than or equal to the low-frequency suction and exhaust noise threshold of the corresponding airflow level in the low-frequency suction and exhaust noise threshold set, then, in conjunction with the set of mechanical component types to be adjusted, damping pads or rubber pads are repeatedly added to each mechanical component of the range hood after the initial low-frequency adjustment until there are no low-frequency suction and exhaust noise data greater than or equal to the low-frequency suction and exhaust noise threshold of the corresponding airflow level;

[0034] Otherwise, there is no need to add shock-absorbing pads or rubber pads; the range hood will be adjusted to low frequency.

[0035] By detecting the stability of mechanical components and dynamically reinforcing them, the generation mechanism of low-frequency vibration noise is blocked from the structural transmission path, which greatly improves the noise reduction efficiency compared to simply covering with damping materials. Combined with the progressive stacking strategy of damping pads, vibration energy is gradually attenuated, ensuring that low-frequency noise remains stable below the building acoustic limit. The effectiveness of noise suppression is ensured by repeatedly stacking damping pads. In the future, the noise prediction function can identify potential problems such as support fatigue in advance, which can extend the equipment life.

[0036] Preferably, step S4 includes the following steps:

[0037] S41. Based on the sampling frequency and the current airflow level type set, collect the suction and exhaust noise data of each airflow level and the high-frequency level during the operation of the range hood after low-frequency adjustment to obtain a real-time high-frequency suction and exhaust noise dataset; based on the real-time high-frequency suction and exhaust noise dataset, predict the high-frequency suction and exhaust noise data at multiple future times to obtain a future high-frequency suction and exhaust noise dataset.

[0038] S42. If there is a high-frequency suction and exhaust noise data in the real-time high-frequency suction and exhaust noise dataset and the future high-frequency suction and exhaust noise dataset that is greater than or equal to the high-frequency suction and exhaust noise threshold of the corresponding airflow setting in the high-frequency suction and exhaust noise threshold set, execute S5; otherwise, use the range hood after low-frequency adjustment as the final range hood.

[0039] Automatic regulation is triggered when current or predicted noise exceeds the threshold, avoiding the noise exceeding the standard problem caused by response delay in traditional methods; it handles high-frequency airflow howling and implements customized noise reduction strategies for high-frequency noise characteristics to improve overall noise reduction efficiency.

[0040] Preferably, step S5 includes the following steps:

[0041] S51. Collect the duct bending arc data, duct diameter data, and average duct air volume data of the range hood after the low-frequency adjustment.

[0042] S52. Input the data collected in S51 into the final high-frequency absorption and discharge noise mapping model for mapping to obtain the current high-frequency absorption and discharge noise data.

[0043] S53. Set the maximum number of repetitions; when the current high-frequency exhaust noise data is greater than or equal to the average value of the high-frequency exhaust noise thresholds in the high-frequency exhaust noise threshold set, repeatedly adjust the pipe bending arc data and pipe diameter data collected in S51, and input the adjusted pipe bending arc data, pipe diameter data, and average pipe air volume data into the final high-frequency exhaust noise mapping model for mapping to obtain the current adjusted high-frequency exhaust noise data; when the number of repetitions is less than or equal to the maximum number of repetitions and the current adjusted high-frequency exhaust noise data is less than the high-frequency exhaust noise threshold of the corresponding air volume setting in the high-frequency exhaust noise threshold set, the adjustment is completed, and the final range hood is obtained; otherwise, the range hood after macro-control is obtained, and proceed to S6;

[0044] Based on multi-parameter coupled analysis of duct curvature, diameter, and airflow, a high-frequency suction and exhaust noise mapping model is used to predict noise levels in real time and compare them with a preset threshold set to ensure that the noise level remains below the safety limit. By iteratively adjusting the duct geometry parameters, energy consumption is reduced while maintaining smoke extraction efficiency. Furthermore, setting a maximum number of repetitions as a termination condition prevents infinite iteration and preserves suboptimal solutions through macroscopic control, ensuring system stability under complex operating conditions. Through triple variable control of airflow, duct diameter, and curvature, active suppression rather than passive absorption of high-frequency noise is achieved.

[0045] Preferably, step S6 includes the following steps:

[0046] S61. Analyze the motor current signal of the range hood after macro-control using FFT, identify the torque pulsation frequency band dominated by harmonics, and obtain the current torque pulsation harmonics; then use an acoustic array microphone to measure the aerodynamic noise corresponding to the range hood after macro-control, extract the standing wave mode frequency, and obtain the current acoustic mode frequency.

[0047] S62. Establish a coupling matrix between torque pulsation harmonics and acoustic mode frequencies, and mark the overlapping frequency bands in the coupling matrix as high-risk resonance regions; set multiple sets of random frequency ratios and SVPWM vector action time allocation ratios to obtain a set of historical random frequency ratios and a set of historical SVPWM vector action time allocation ratios.

[0048] S63. In conjunction with the historical random frequency ratio set and the historical SVPWM vector action time allocation ratio set, introduce the random frequency of the historical random frequency ratio into the SVPWM carrier cycle corresponding to the range hood after macro-control, and combine it with the historical SVPWM vector action time allocation ratio; then collect the corresponding high-risk resonance zone ratio data, real-time high-frequency suction and exhaust noise data, and the high-frequency suction and exhaust noise data corresponding to the future time. The sum of the number of high-frequency suction and exhaust noise data that are greater than or equal to the high-frequency suction and exhaust noise threshold of the corresponding airflow level in the high-frequency suction and exhaust noise threshold set is used to obtain the high-frequency noise number set after macro-control and the current high-risk resonance zone ratio data.

[0049] Based on the historical random frequency ratio set, the historical SVPWM vector action time allocation ratio set, the high-frequency noise number set after macro-control, and the current high-risk resonance zone proportion data, a high-frequency noise number mapping model and a high-risk resonance zone proportion mapping model are constructed respectively.

[0050] S64. Adjust the current random frequency ratio and SVPWM vector action time allocation ratio of the range hood after macro-control, and input the adjusted data into the high-frequency noise number mapping model and the high-risk resonance zone proportion mapping model respectively to obtain the current high-frequency noise number and the current high-risk resonance zone proportion.

[0051] S65. When the current number of high-frequency noises is not 0 or the current proportion of high-risk resonance zone is not 0, repeat S64 until the current number of high-frequency noises and the current proportion of high-risk resonance zone are both 0, and obtain the final range hood.

[0052] By analyzing the motor current harmonics and aerodynamic noise modes captured by the acoustic array microphone using FFT, a coupling matrix of torque pulsation and acoustic standing waves is established to accurately identify high-risk resonant frequency bands. This allows for the location of the resonance source of mechanical vibration and aerodynamic noise, avoiding the limitations of single-dimensional governance. Based on historical random frequency ratios and vector action time allocation ratios, the SVPWM carrier period parameters are dynamically adjusted, and the number of high-frequency noises and the proportion of the resonant region are predicted through a mapping model. An iterative optimization strategy is adopted to gradually eliminate risky frequency bands, taking into account both switching losses and harmonic suppression requirements. Real-time monitoring of high-frequency noise datasets and future noise prediction sets, combined with threshold conditions, enables advanced early warning.

[0053] The suction and exhaust noise control system based on SVPWM space vector control includes a low-frequency suction and exhaust noise data acquisition module, a low-frequency suction and exhaust noise data judgment module, a low-frequency suction and exhaust noise suppression module, a high-frequency suction and exhaust noise data acquisition and judgment module, a range hood duct parameter adjustment module, and an SVPWM space vector adjustment module.

[0054] The present invention has the following beneficial effects:

[0055] 1. This invention achieves low-frequency noise control by integrating mechanical vibration reduction, fluid optimization, and power electronic control technologies; it effectively suppresses structural vibration noise through mechanical component stability testing and vibration damping pad reinforcement, avoiding resonance degradation problems caused by traditional single noise reduction methods; a high-frequency noise mapping model constructed based on pipeline parameters and airflow data can predict aerodynamic noise risks in advance, enabling preventative adjustments; SVPWM is employed to break the resonance condition between motor torque pulsation and aerodynamic noise by randomly redistributing the switching frequency and vector action time, reducing the proportion of the high-frequency resonance region to a preset threshold; and by collecting noise data from multiple airflow levels in real time and combining it with future time predictions to form a feedback closed loop, it ensures that the noise level remains below the threshold during long-term use, avoiding performance degradation caused by traditional fixed parameters.

[0056] 2. This invention utilizes mechanical component stability testing and dynamic reinforcement to block the generation mechanism of low-frequency vibration noise from the structural transmission path, significantly improving noise reduction efficiency compared to simple damping material coverage. Combined with a progressive stacking strategy of damping pads, vibration energy is gradually attenuated, ensuring low-frequency noise remains consistently below building acoustic limits. Repeatedly stacking damping pads ensures the effectiveness of noise suppression. Future noise prediction functions can identify potential problems such as bracket fatigue in advance, extending equipment lifespan. Through the combination of stability reinforcement and damping materials, the invention eliminates the pain points of traditional range hoods, such as "humming noise at low speeds" and "metallic vibration noise at high speeds," resulting in a more uniform noise spectrum across all speeds and a significantly improved subjective sense of quietness for users.

[0057] 3. In this invention, a multi-parameter coupled analysis based on the pipe curvature, diameter and air volume is used to predict the noise value in real time through a high-frequency suction and exhaust noise mapping model, and compare it with a preset threshold set to ensure that the noise is always below the safety limit; by iteratively adjusting the pipe geometric parameters, energy consumption is reduced while ensuring smoke exhaust efficiency; in addition, a maximum number of repetitions is set as a termination condition to prevent infinite iteration and retain suboptimal solutions through macro-control, ensuring the stability of the system under complex working conditions.

[0058] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of the invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a flowchart illustrating the suction and discharge noise control method based on SVPWM space vector control of the present invention.

[0061] Figure 2 This is a schematic diagram of the intake and exhaust noise control system based on SVPWM space vector control of the present invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the invention, and not all embodiments. Based on the embodiments of the invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the invention.

[0063] Example 1

[0064] Please see Figure 1 This embodiment describes a method for controlling intake and exhaust noise based on SVPWM space vector control, which includes the following steps:

[0065] S1. Collect data on suction and exhaust noise at multiple airflow levels and low frequencies during the operation of the range hood to be controlled; collect duct data from multiple existing range hoods to construct the final high-frequency suction and exhaust noise mapping model.

[0066] S1 includes the following steps:

[0067] S11. Set the sampling frequency, the range hood to be adjusted, and the corresponding airflow level type to obtain the current airflow level type set; install and run the range hood to be adjusted.

[0068] S12. After the range hood to be controlled is installed and put into operation, collect the suction and exhaust noise data of each air volume level and low frequency during the operation of the range hood to be controlled according to the sampling frequency and the current air volume level type set, and obtain the real-time low frequency suction and exhaust noise dataset.

[0069] S13. Based on the current set of airflow level types, set different airflow levels and suction and exhaust noise thresholds at high and low frequencies to obtain a set of high-frequency suction and exhaust noise thresholds and a set of low-frequency suction and exhaust noise thresholds.

[0070] Specifically, in the selection of the above thresholds, low-frequency noise (20-500Hz) needs to be strictly limited; the nighttime low-frequency noise limit in residential areas is ≤35dB(A), and the sound pressure levels in octave bands such as 31.5Hz and 63Hz must meet the standards to avoid structural sound transmission hazards; in addition, the upper limit of the high-frequency noise (>500Hz) threshold is 65dB(A), but the Class A quiet certification requires ≤52dB(A); the high-frequency noise threshold increases with the setting: low setting (simmering soup) ≤ 45dB(A), medium setting (frying) ≤55dB(A), high setting (stir-frying) ≤65dB(A), conforming to the physical law that increased air volume leads to increased turbulent noise; Low frequency threshold (20-250Hz): strictly controlled ≤40dB(A) to avoid resonance causing sound transmission through building structures (such as wall vibration); High frequency threshold (>500Hz): slightly higher is allowed but whistling must be suppressed, and the threshold is based on aeroacoustic simulation and muffler performance verification data;

[0071] S14. In conjunction with the high-frequency suction and exhaust noise threshold set, collect the existing range hoods' pipe bending curvature data, pipe diameter data, and average pipe air volume data for those whose average high-frequency suction and exhaust noise data are less than or greater than or equal to the corresponding high-frequency suction and exhaust noise threshold.

[0072] S15. Based on the pipe bending radius data, pipe diameter data, average pipe air volume data, and corresponding average high-frequency suction and exhaust noise data collected in S14, construct the final high-frequency suction and exhaust noise mapping model.

[0073] S15 includes the following steps:

[0074] S151. Construct an initial high-frequency absorption and exhaust noise mapping model;

[0075] The structure of the initial high-frequency absorption and dissipation noise mapping model is as follows:

[0076] (1) Input layer (3 channels): Channel 1: Pipe curvature (normalized to 0-1 range); Channel 2: Pipe diameter (mm standardized unit); Channel 3: Average air volume (m³ / min standardized unit).

[0077] (2) Feature extraction module: including 1D-CNN layer group (extracting local fluid features) and spatiotemporal attention layer (capturing radian-noise correlation);

[0078] The 1D-CNN layer group is as follows:

[0079] Convolution kernel: [32, 64], kernel_size (kernel size) = 3, stride (stride) = 1;

[0080] Activation function: LeakyReLU (α=0.1);

[0081] Pooling layer: MaxPool1D(pool_size(pooling layer size) = 2);

[0082] Spatiotemporal attention layer: multi-head attention mechanism (4 heads); position encoding uses a sine curve;

[0083] (3) Regression prediction module: including fully connected layer group: FC1: 128 neurons + BatchNorm + Dropout (0.3); FC2: 64 neurons + Swish activation; output layer: linear activation (predicting noise dB value);

[0084] (4) Loss function: Composite loss L = 0.7 * MAE (mean absolute error) + 0.3 * spectral correlation coefficient loss; where, spectral correlation coefficient loss is a loss function used to measure the correlation between the predicted spectrum and the real spectrum in a signal processing or machine learning model; it quantifies the consistency between the two by calculating the correlation coefficient between the predicted spectrum and the real spectrum, and is often used in fields such as speech enhancement and image reconstruction that require maintaining spectral features; the predicted spectrum and the real spectrum refer to the spectrum of the high-frequency absorption and exhaust noise (predicted data) obtained by the initial high-frequency absorption and exhaust noise mapping model during the training process and the spectrum of the corresponding average high-frequency absorption and exhaust noise data (real data);

[0085] The formulas for calculating the spectral correlation coefficient and the spectral correlation coefficient loss are as follows:

[0086] ;

[0087] ;

[0088] In the formula, These represent the spectral correlation coefficient and the spectral correlation coefficient loss, respectively. These represent the actual spectrum and the predicted spectrum in the frequency domain k, respectively; These represent the mean of the actual spectrum and the mean of the predicted spectrum, respectively. Represents the upper frequency domain limit of the actual spectrum and the predicted spectrum;

[0089] S152. The initial high-frequency suction and exhaust noise mapping model is trained and tested using the pipe bending radius data, pipe diameter data, average pipe air volume data, and corresponding average high-frequency suction and exhaust noise data collected in S14; after training and testing, the final high-frequency suction and exhaust noise mapping model is obtained.

[0090] Specifically, it includes the following steps:

[0091] S1521. Set the training data ratio (generally 7:3); use the training data ratio to partition the existing pipe curvature dataset, existing pipe diameter dataset, existing average pipe air volume dataset, and existing average high-frequency suction and exhaust noise dataset to obtain the existing pipe curvature training dataset, existing pipe diameter training dataset, existing average pipe air volume training dataset, existing average high-frequency suction and exhaust noise training dataset, existing pipe curvature test dataset, existing pipe diameter test dataset, existing average pipe air volume test dataset, and existing average high-frequency suction and exhaust noise test dataset.

[0092] S1522. Set a training error threshold (which can be adaptively set according to the actual training situation); input the existing pipeline curvature training dataset, the existing pipeline diameter training dataset, the existing average pipeline air volume training dataset as training data, and the existing average high-frequency suction and exhaust noise training dataset as training labels into the initial high-frequency suction and exhaust noise mapping model for training; during the training process, when the training error is less than the training error threshold, stop training and obtain the trained high-frequency suction and exhaust noise mapping model; otherwise, continue training;

[0093] S1523. Set the test accuracy threshold (adaptive setting based on actual training conditions); input the existing pipe bending arc test dataset, existing pipe diameter test dataset, and existing average pipe air volume test dataset as test data, and the existing average high-frequency suction and exhaust noise test dataset as test labels into the trained high-frequency suction and exhaust noise mapping model for testing; after the test is completed, obtain the test accuracy data; when the test accuracy data is greater than or equal to the test accuracy threshold, use the trained high-frequency suction and exhaust noise mapping model as the final high-frequency suction and exhaust noise mapping model; otherwise, return to S1522 to continue training until the test accuracy data is greater than or equal to the test accuracy threshold.

[0094] The initial high-frequency suction and exhaust noise mapping model constructed in this scheme has the ability to extract deep features of the strong nonlinear relationship between pipe geometric parameters (radius / diameter) and fluid noise; and can improve feature reuse efficiency when the amount of industrial data is limited; and can simultaneously process structured parameters (diameter / air volume) and spatial features (curvature).

[0095] By establishing a coupling relationship model between pipe geometric parameters (bending radius, diameter) and fluid characteristics (airflow), the generation mechanism of high-frequency howling noise (800Hz-5kHz) is quantified into a calculable fluid dynamic index for the first time. Compared with the traditional single threshold alarm method, it can identify the sudden change in turbulent noise caused by the combination of specific curvature (such as a 90° sharp bend) and critical diameter (below φ150mm). The high-frequency suction and exhaust noise mapping model provides a three-dimensional optimization space for range hood pipe design: it identifies that the curved transition section (curvature radius ≥ 2 times the pipe diameter) can reduce eddy noise by 12dB(A); it establishes the optimal pipe diameter selection matrix under different airflow levels (8-15m³ / min) to avoid airflow stripping noise caused by excessive flow velocity; and it achieves active suppression of high-frequency noise rather than passive absorption through the triple variable control of airflow, pipe diameter, and curvature.

[0096] The initial high-frequency suction and discharge noise mapping model constructed in this scheme has the ability to extract deep features of the strong nonlinear relationship between pipeline geometric parameters and fluid noise; and can improve feature reuse efficiency when the amount of industrial data is limited; and can process structured parameters and spatial features simultaneously.

[0097] Specifically, during the noise data collection process, each setting of the range hood to be adjusted should be maintained for at least 5 minutes to cover typical operating conditions; before data collection, the equipment should be calibrated using a sound calibrator (such as AWA6021A) to ensure that the decibel error is ≤0.5dB, and a 20-second standard sound source should be recorded as a reference; the continuous recording function of the sound level meter or APP should be activated, the sampling interval should be set to 1 second (equal interval mode), and the timestamp, real-time decibel value and maximum noise peak value should be recorded;

[0098] The real-time high-frequency suction and exhaust noise dataset includes airflow whistling (high-frequency) noise data, etc.; the real-time low-frequency suction and exhaust noise dataset includes mechanical vibration (low-frequency) noise data, etc.

[0099] By collecting data across frequency bands (high-frequency airflow whistling and low-frequency mechanical vibration), the system achieves precise location and classification of noise sources, providing data support for targeted noise reduction. Combined with continuous sampling at multiple speed settings, it comprehensively covers the noise characteristics of range hoods under different operating conditions. Dynamic data acquisition based on the fan speed setting type can cover the noise control needs of different cooking scenarios (such as stir-frying and stewing). High-frequency and low-frequency datasets are stored separately, facilitating subsequent optimization of the range hood to be controlled more accurately based on frequency characteristics. Pre-calibration using a sound calibrator (error ≤ 0.5dB) and recording with a standard sound source benchmark ensure data comparability and laboratory-level accuracy. Equal-interval sampling (1 second / time) combined with timestamp recording provides corresponding time-series data for subsequent prediction of future suction and exhaust noise. The 5-minute / speed sampling duration design effectively captures steady-state and non-steady-state noise, avoiding omissions of operating conditions caused by short-term sampling.

[0100] S2. If there is low-frequency suction and exhaust noise data greater than or equal to the corresponding threshold in the suction and exhaust noise data collected in S1 and the corresponding future suction and exhaust noise data, proceed to S3; otherwise, treat the range hood to be adjusted as the range hood after low-frequency adjustment and execute S4.

[0101] S2 includes the following steps:

[0102] S21. When there is a real-time low-frequency suction and exhaust noise data in the real-time low-frequency suction and exhaust noise data that is greater than or equal to the low-frequency suction and exhaust noise threshold of the corresponding airflow level in the low-frequency suction and exhaust noise threshold set, proceed to S3.

[0103] Otherwise, based on the real-time low-frequency suction and discharge noise dataset, the low-frequency suction and discharge noise data at multiple future times are predicted to obtain the future low-frequency suction and discharge noise dataset.

[0104] S22. Adjust the range hood to be controlled according to the future low-frequency suction and exhaust noise dataset;

[0105] S22 includes the following steps:

[0106] S221. When there is a low-frequency suction and exhaust noise data in the future low-frequency suction and exhaust noise data that is greater than or equal to the low-frequency suction and exhaust noise threshold of the corresponding airflow setting in the low-frequency suction and exhaust noise threshold set, proceed to S3; otherwise, there is no need to adjust the range hood to be adjusted, and the range hood to be adjusted is used as the low-frequency adjusted range hood and S4 is executed.

[0107] The method for predicting low-frequency intake and exhaust noise data at multiple future times includes linear regression, etc.

[0108] S3. Reinforce and dampen the various mechanical components of the range hood to be adjusted until the real-time low-frequency suction and exhaust noise data and the corresponding future suction and exhaust noise data both meet the corresponding thresholds, and obtain the range hood after low-frequency adjustment and execute S4.

[0109] S3 includes the following steps:

[0110] S31. Set several types of mechanical components for the installation of range hoods to obtain a set of mechanical component types to be controlled; and, in conjunction with the set of mechanical component types to be controlled, perform stability testing and reinforcement on each mechanical component of the range hood to be controlled.

[0111] For example, as shown in Table 1 below:

[0112] Table 1

[0113]

[0114] S32. After stability testing and reinforcement are completed, the range hood after initial low-frequency adjustment is obtained;

[0115] If, after the initial low-frequency adjustment, there are still low-frequency suction and exhaust noise data at each airflow level and at the low frequency, as well as the corresponding future suction and exhaust noise data, that are greater than or equal to the low-frequency suction and exhaust noise threshold of the corresponding airflow level in the low-frequency suction and exhaust noise threshold set, then, in conjunction with the set of mechanical component types to be adjusted, damping pads or rubber pads are repeatedly added to each mechanical component of the range hood after the initial low-frequency adjustment until there are no low-frequency suction and exhaust noise data greater than or equal to the low-frequency suction and exhaust noise threshold of the corresponding airflow level;

[0116] Otherwise, there is no need to add shock-absorbing pads or rubber pads; the range hood will be adjusted to low frequency.

[0117] By using mechanical component stability testing and dynamic reinforcement (such as screw torque calibration and bracket angle adjustment), the generation mechanism of low-frequency vibration noise is blocked from the structural transmission path. Compared with simple damping material coverage, the noise reduction efficiency is improved by more than 30%. Combined with the progressive stacking strategy of damping pads, vibration energy is gradually attenuated, ensuring that low-frequency noise (20-250Hz) is consistently below the building acoustic limit of 35dB(A). The effectiveness of noise suppression is ensured by repeatedly stacking damping pads. In the future, the noise prediction function can identify potential problems such as bracket fatigue in advance, which can extend the equipment life. Through the combination of stability reinforcement and damping materials, the pain points of traditional range hoods such as "humming noise at low speed" and "metallic vibration noise at high speed" are eliminated, making the noise spectrum uniform at each speed and improving the user's subjective sense of quietness by 40%.

[0118] S4. Collect suction and exhaust noise data at multiple airflow levels and high frequencies during the operation of the range hood after low-frequency adjustment; if there is high-frequency suction and exhaust noise data greater than or equal to the corresponding threshold in the suction and exhaust noise data collected in S4 and the corresponding future suction and exhaust noise data, execute S5; otherwise, use the range hood after low-frequency adjustment as the final range hood.

[0119] S4 includes the following steps:

[0120] S41. Based on the sampling frequency and the current airflow level type set, collect the suction and exhaust noise data of each airflow level and the high-frequency level during the operation of the range hood after low-frequency adjustment to obtain a real-time high-frequency suction and exhaust noise dataset; based on the real-time high-frequency suction and exhaust noise dataset, predict the high-frequency suction and exhaust noise data at multiple future times to obtain a future high-frequency suction and exhaust noise dataset.

[0121] S42. If there is a high-frequency suction and exhaust noise data in the real-time high-frequency suction and exhaust noise dataset and the future high-frequency suction and exhaust noise dataset that is greater than or equal to the high-frequency suction and exhaust noise threshold of the corresponding airflow setting in the high-frequency suction and exhaust noise threshold set, execute S5; otherwise, use the range hood after low-frequency adjustment as the final range hood.

[0122] Automatic regulation is triggered when current or predicted noise exceeds the threshold, avoiding the noise exceeding the standard problem caused by response delay in traditional methods; it handles high-frequency airflow howling and implements customized noise reduction strategies for high-frequency noise characteristics to improve overall noise reduction efficiency.

[0123] S5. Adjust the duct parameters of the range hood after low-frequency adjustment by a preset number of repetitions in conjunction with the final high-frequency suction and exhaust noise mapping model, and input them into the final high-frequency suction and exhaust noise mapping model for mapping; if the mapping result meets the corresponding threshold within the preset number of repetitions, the final range hood is obtained; otherwise, the range hood after macro-control is obtained.

[0124] S5 includes the following steps:

[0125] S51. Collect the duct bending arc data, duct diameter data, and average duct air volume data of the range hood after the low-frequency adjustment.

[0126] S52. Input the data collected in S51 into the final high-frequency absorption and discharge noise mapping model for mapping to obtain the current high-frequency absorption and discharge noise data.

[0127] S53. Set the maximum number of repetitions; when the current high-frequency exhaust noise data is greater than or equal to the average value of the high-frequency exhaust noise thresholds in the high-frequency exhaust noise threshold set, repeatedly adjust the pipe bending arc data and pipe diameter data collected in S51, and input the adjusted pipe bending arc data, pipe diameter data, and average pipe air volume data into the final high-frequency exhaust noise mapping model for mapping to obtain the current adjusted high-frequency exhaust noise data; when the number of repetitions is less than or equal to the maximum number of repetitions and the current adjusted high-frequency exhaust noise data is less than the high-frequency exhaust noise threshold of the corresponding air volume setting in the high-frequency exhaust noise threshold set, the adjustment is completed, and the final range hood is obtained; otherwise, the range hood after macro-control is obtained, and proceed to S6;

[0128] Based on multi-parameter coupled analysis of pipe curvature, diameter, and airflow, a high-frequency suction and exhaust noise mapping model is used to predict noise values ​​in real time and compare them with a preset threshold set (including current, real-time, and future noise conditions) to ensure that the noise level is always below the safety limit. Replacing right-angle bends with 45° angled bends can reduce wind pressure loss by 60%. Combined with pipe diameter optimization, the wind speed is controlled below 8 m / s, effectively suppressing turbulent noise peaks. By iteratively adjusting pipe geometry parameters (e.g., optimizing curvature from 1.57 rad to 0.79 rad and increasing pipe diameter by 25%), energy consumption is reduced while maintaining smoke extraction efficiency. In addition, a maximum number of repetitions is set as a termination condition to prevent infinite iteration and retain suboptimal solutions through macroscopic control, ensuring the system's stability under complex operating conditions (such as oil accumulation and airflow fluctuations). For example, the U-shaped oil trap design can prevent external air backflow and maintain negative pressure stability. Combined with maintenance recommendations for regularly cleaning oil and lubricating components, the equipment life is extended and potential hazards are reduced.

[0129] By setting differentiated high-frequency / low-frequency noise thresholds, combined with real-time data monitoring and future noise trend prediction, an upgrade from passive response to active intervention is achieved. Automatic control is triggered when current or predicted noise exceeds the threshold, avoiding noise exceedance issues caused by response delays in traditional methods. High-frequency airflow whistling and low-frequency mechanical vibration are processed separately, and customized noise reduction strategies are implemented for different frequency band noise characteristics (such as high-frequency turbulent noise and low-frequency structural sound transmission), improving overall noise reduction efficiency. For example, high-frequency noise can be suppressed by optimizing the streamlined design of the air duct, while low-frequency noise is blocked using a vibration-damping base. Dynamic threshold management based on the fan speed type set ensures the optimal balance between suction and noise in different cooking scenarios (such as high fan speed for stir-frying and low fan speed for stewing). Through a dual guarantee mechanism of real-time data verification and future noise prediction, the false judgment rate (such as mis-control triggered by brief interference noise) is significantly reduced, while avoiding control failure due to sensor malfunction or data anomalies.

[0130] S6. Adjust the current random frequency ratio and SVPWM vector action time allocation ratio of the range hood after macro-control until the proportion of high-risk resonance zone and the number of high-frequency noise meet the conditions.

[0131] S6 includes the following steps:

[0132] S61. Analyze the motor current signal of the range hood after macroscopic control using FFT to identify 6 K ± n The torque pulsation frequency band dominated by subharmonics ( K The fundamental frequency, n The switching frequency is multiplied to obtain the current torque pulsation harmonics; then, the aerodynamic noise of the range hood after macroscopic control is measured using an acoustic array microphone, and the standing wave mode frequency in the range of 100-2000Hz is extracted to obtain the current acoustic mode frequency;

[0133] S62. Establish a coupling matrix between torque pulsation harmonics and acoustic mode frequencies, and mark the overlapping frequency bands in the coupling matrix as high-risk resonance regions (such as around 800Hz); set multiple sets of random frequency ratios and SVPWM vector action time allocation ratios to obtain historical random frequency ratio sets and historical SVPWM vector action time allocation ratio sets.

[0134] S63. In conjunction with the historical random frequency ratio set and the historical SVPWM vector action time allocation ratio set, introduce the random frequency of the historical random frequency ratio into the SVPWM carrier cycle corresponding to the range hood after macro-control, and combine it with the historical SVPWM vector action time allocation ratio; then collect the corresponding high-risk resonance zone proportion data, the real-time high-frequency suction and exhaust noise dataset, and the sum of the number of high-frequency suction and exhaust noise data in the future high-frequency suction and exhaust noise dataset that are greater than or equal to the high-frequency suction and exhaust noise threshold of the corresponding airflow level in the high-frequency suction and exhaust noise threshold set, to obtain the high-frequency noise number set after macro-control and the current high-risk resonance zone proportion data;

[0135] Based on the historical random frequency ratio set, the historical SVPWM vector action time allocation ratio set, the set of high-frequency noise numbers after macro-control, and the current high-risk resonance zone proportion data, mapping models between the random frequency ratio, the SVPWM vector action time allocation ratio, the number of high-frequency noises, and the proportion of high-risk resonance zones are constructed respectively, resulting in the high-frequency noise number mapping model and the high-risk resonance zone proportion mapping model.

[0136] The high-frequency noise number mapping model and the high-risk resonance zone proportion mapping model can be selected from the Random Forest Regression model and the 1D Convolutional Neural Network (1D-CNN), respectively.

[0137] S64. Adjust the current random frequency ratio and SVPWM vector action time allocation ratio of the range hood after macro-control, and input the adjusted data into the high-frequency noise number mapping model and the high-risk resonance zone proportion mapping model respectively to obtain the current high-frequency noise number and the current high-risk resonance zone proportion.

[0138] S65. When the current number of high-frequency noises is not 0 or the current proportion of high-risk resonance zone is not 0, repeat S64 until the current number of high-frequency noises and the current proportion of high-risk resonance zone are both 0, and obtain the final range hood.

[0139] For example, as follows:

[0140] Pipeline structure optimization stage:

[0141] Initial parameters: The detected pipe bending radius is 1.57 rad (90° right angle bend), pipe diameter is 200 mm, average air volume is 12 m³ / min at air volume setting 3, and the initial value of high frequency noise is 78 dB(A), which exceeds the threshold average value of 65 dB(A).

[0142] Adjustment process:

[0143] The elbow was changed to a 45° bevel (0.79 rad), the pipe diameter was increased to 250 mm, and the wind pressure loss was reduced from 100 Pa to 40 Pa. After 3 iterations, the noise was reduced to 62 dB(A), and the maximum value of the real-time noise dataset was 63 dB(A), which was lower than the threshold of 68 dB(A).

[0144] Electro-acoustic resonance suppression stage

[0145] Harmonic analysis: FFT detected the 6K±n harmonic of the motor current (fundamental frequency 50Hz, switching frequency 8kHz), with the dominant frequency band being 800Hz±200Hz; the acoustic array synchronously captured the aerodynamic noise standing wave mode frequency of 820Hz, and the coupling matrix marked this frequency band as a high-risk area;

[0146] SVPWM optimization:

[0147] A five-segment modulation sequence is adopted, the carrier frequency is randomized (7.5kHz~8.5kHz±15%), and the vector action time allocation ratio is adjusted to [0.4, 0.3, 0.3];

[0148] The mapping model predicts that the number of high-frequency noises decreases from 5 to 0, the proportion of the resonant region decreases from 12% to zero, and the final output noise stabilizes at 58 dB(A).

[0149] The relevant parameters before and after optimization are shown in Table 2 below:

[0150] Table 2

[0151] parameter Optimize the previous value Optimized value Threshold requirements pipe bending arc 1.57 rad 0.79rad / High frequency noise peak 78dB(A) 58dB(A) <65dB(A) resonant region proportion 12% 0% 0% SVPWM switching frequency fluctuation Fixed 8kHz 7.5-8.5kHz /

[0152] By analyzing the motor current harmonics (6K±n) and the aerodynamic noise modes (100-2000Hz) captured by the acoustic array microphone using FFT, a coupling matrix between torque pulsation and acoustic standing waves is established. This accurately identifies high-risk resonant frequency bands (such as around 800Hz), allowing for the location of the resonance source of mechanical vibration and aerodynamic noise, thus avoiding the limitations of single-dimensional governance. Based on historical random frequency ratios and vector action time allocation ratios, the SVPWM carrier period parameters are dynamically adjusted, and the number of high-frequency noises and the proportion of the resonant region are predicted through a mapping model. An iterative optimization strategy is used to gradually eliminate risky frequency bands, balancing switching losses and harmonic suppression requirements. Real-time monitoring of high-frequency noise datasets and future noise prediction sets, combined with threshold conditions, enables proactive early warning. For example, when the acoustic mode frequency approaches the mechanical resonance point, parameter adjustments are automatically triggered to prevent structural fatigue caused by prolonged resonance of the equipment. By optimizing the SVPWM vector allocation ratio (such as adjusting the modulation ratio or switching frequency), torque pulsation is reduced while ensuring airflow requirements, keeping the noise level consistently below the national standard limit (such as 85dB(A)) and reducing additional power consumption of the motor.

[0153] Example 2

[0154] Please see Figure 2 This embodiment discloses a suction and exhaust noise control system based on SVPWM space vector control. The system can implement the method of the above embodiment, including a low-frequency suction and exhaust noise data acquisition module, a low-frequency suction and exhaust noise data determination module, a low-frequency suction and exhaust noise suppression module, a high-frequency suction and exhaust noise data acquisition and determination module, a range hood duct parameter adjustment module, and an SVPWM space vector adjustment module.

[0155] The low-frequency suction and exhaust noise data acquisition module collects suction and exhaust noise data at multiple airflow levels and low frequencies during the operation of the range hood to be controlled; it also collects multiple sets of existing range hood duct bending curvature and diameter data, average duct airflow data, and corresponding high-frequency suction and exhaust noise data to construct the final high-frequency suction and exhaust noise mapping model.

[0156] If the low-frequency suction and exhaust noise data collected by the low-frequency suction and exhaust noise data acquisition module and the corresponding future suction and exhaust noise data contain low-frequency suction and exhaust noise data greater than or equal to the corresponding threshold, the low-frequency suction and exhaust noise suppression module will be entered; otherwise, the range hood to be adjusted will be treated as a range hood after low-frequency adjustment and the high-frequency suction and exhaust noise data acquisition and judgment module will be executed.

[0157] The low-frequency suction and exhaust noise suppression module reinforces and dampens the various mechanical components of the range hood to be regulated until the real-time low-frequency suction and exhaust noise data and the corresponding future time data meet the corresponding thresholds. The low-frequency regulated range hood is then used to execute the high-frequency suction and exhaust noise data acquisition and judgment module.

[0158] The high-frequency suction and exhaust noise data acquisition and judgment module collects suction and exhaust noise data at multiple airflow levels and at high frequencies during the operation of the range hood after low-frequency adjustment. If the collected suction and exhaust noise data and the corresponding future suction and exhaust noise data contain high-frequency suction and exhaust noise data that is greater than or equal to the corresponding threshold, the range hood duct parameter adjustment module is executed; otherwise, the range hood after low-frequency adjustment is used as the final range hood.

[0159] The range hood duct parameter adjustment module, in conjunction with the final high-frequency suction and exhaust noise mapping model, adjusts the duct parameters of the range hood after low-frequency adjustment and inputs them into the final high-frequency suction and exhaust noise mapping model for mapping. If the mapping result meets the corresponding threshold, the final range hood is obtained; otherwise, the range hood after macro-control is obtained.

[0160] The SVPWM space vector adjustment module adjusts the current random frequency ratio and SVPWM vector action time allocation ratio of the range hood after macro-control until the proportion of high-risk resonance zone and the number of high-frequency noise meet the conditions, thus obtaining the final range hood.

[0161] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0162] The preferred embodiments of the invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for regulating suction and exhaust noise based on SVPWM space vector control, characterized in that, The method comprises the following steps: S1, collecting the suction and exhaust noise data of the to-be-controlled range hood under multiple air volume gears and low frequencies during operation; Collecting multiple sets of existing range hood pipeline data to construct a final high-frequency suction and exhaust noise mapping model; S2, if the suction and exhaust noise data collected in S1 and the corresponding future time suction and exhaust noise data contain low-frequency suction and exhaust noise data greater than or equal to the corresponding threshold, proceed to S3; otherwise, take the to-be-controlled range hood as a low-frequency adjusted range hood and execute S4; S3, reinforce and dampen each mechanical component of the to-be-controlled range hood until the real-time low-frequency suction and exhaust noise data and the corresponding future time suction and exhaust noise data meet the corresponding threshold, obtaining a low-frequency adjusted range hood and executing S4; S4, collecting the suction and exhaust noise data of the low-frequency adjusted range hood under multiple air volume gears and high frequencies during operation; If the suction and exhaust noise data collected in S4 and the corresponding future time suction and exhaust noise data contain high-frequency suction and exhaust noise data greater than or equal to the corresponding threshold, execute S5; otherwise, take the low-frequency adjusted range hood as the final range hood; S5, cooperate with the final high-frequency suction and exhaust noise mapping model to adjust the pipeline parameters of the low-frequency adjusted range hood for a preset number of times and input them into the final high-frequency suction and exhaust noise mapping model for mapping; If the mapping result meets the corresponding threshold within the preset number of times, obtain the final range hood; Otherwise, obtain the macro-control range hood; S6, adjust the current random frequency proportion and SVPWM vector action time allocation proportion of the macro-control range hood until the high-risk resonance area proportion and the number of high-frequency noises meet the conditions.

2. The SVPWM space vector control based suction and exhaust noise regulation method according to claim 1, characterized in that, The S1 comprises the following steps: S11, set the sampling frequency, the to-be-controlled range hood, and the corresponding air volume gear type to obtain the current air volume gear type set; install and run the to-be-controlled range hood; S12, after the to-be-controlled range hood is installed and run, collect the suction and exhaust noise data of each air volume gear and low frequency during the operation of the to-be-controlled range hood according to the sampling frequency and the current air volume gear type set to obtain a real-time low-frequency suction and exhaust noise data set; S13, cooperate with the current air volume gear type set to set the suction and exhaust noise thresholds of different air volume gears and high and low frequencies to obtain a high-frequency suction and exhaust noise threshold set and a low-frequency suction and exhaust noise threshold set; S14, cooperate with the high-frequency suction and exhaust noise threshold set to collect the pipeline bending radius data, pipeline diameter data, and average pipeline air volume data of the existing range hood whose average high-frequency suction and exhaust noise data is less than and greater than or equal to the corresponding high-frequency suction and exhaust noise threshold, respectively; S15, construct a final high-frequency suction and exhaust noise mapping model according to the pipeline bending radius data, pipeline diameter data, average pipeline air volume data, and corresponding average high-frequency suction and exhaust noise data collected in S14.

3. The SVPWM space vector control based suction and exhaust noise regulation method according to claim 2, characterized in that, The S15 comprises the following steps: S151, construct an initial high-frequency suction and exhaust noise mapping model; S152, training and testing the initial high-frequency suction and exhaust noise mapping model by using the pipeline bending radian data, pipeline diameter data, average pipeline air volume data, and corresponding average high-frequency suction and exhaust noise data collected in S14; after the training and testing are completed, a final high-frequency suction and exhaust noise mapping model is obtained.

4. The SVPWM space vector control based suction and exhaust noise regulation method according to claim 3, characterized in that, The S2 includes the following steps: S21, when there is real-time low-frequency suction and exhaust noise data greater than or equal to the low-frequency suction and exhaust noise threshold value corresponding to the air volume gear in the low-frequency suction and exhaust noise threshold value set in the real-time low-frequency suction and exhaust noise data set, S3 is entered; Otherwise, the low-frequency suction and exhaust noise data of multiple future time points are predicted according to the real-time low-frequency suction and exhaust noise data set, and a future low-frequency suction and exhaust noise data set is obtained; S22, the to-be-regulated range hood is regulated according to the future low-frequency suction and exhaust noise data set.

5. The SVPWM space vector control based suction and exhaust noise regulation method according to claim 4, characterized in that, The S22 includes the following steps: S221, when there is low-frequency suction and exhaust noise data greater than or equal to the low-frequency suction and exhaust noise threshold value corresponding to the air volume gear in the low-frequency suction and exhaust noise threshold value set in the future low-frequency suction and exhaust noise data set, S3 is entered; otherwise, the to-be-regulated range hood does not need to be regulated, and the to-be-regulated range hood is taken as a low-frequency regulated range hood and S4 is executed.

6. The SVPWM space vector control based suction and exhaust noise regulation method according to claim 5, characterized in that, The S3 includes the following steps: S31, a plurality of installation mechanical component types of range hoods are set, and a to-be-regulated mechanical component type set is obtained; each mechanical component of the to-be-regulated range hood is detected for stability and reinforced in cooperation with the to-be-regulated mechanical component type set; S32, after the stability detection and reinforcement are completed, an initial low-frequency regulated range hood is obtained; If there is still low-frequency suction and exhaust noise data greater than or equal to the low-frequency suction and exhaust noise threshold value corresponding to the air volume gear in the low-frequency suction and exhaust noise threshold value set in the suction and exhaust noise data of each air volume gear and low frequency and the corresponding future time point suction and exhaust noise data in the initial low-frequency regulated range hood operation process, cooperate with the to-be-regulated mechanical component type set, repeatedly add damping washers at each mechanical component of the initial low-frequency regulated range hood until there is no low-frequency suction and exhaust noise data greater than or equal to the low-frequency suction and exhaust noise threshold value corresponding to the air volume gear; Otherwise, no damping washer needs to be added; a low-frequency regulated range hood is obtained.

7. The SVPWM space vector control based suction and exhaust noise regulation method according to claim 6, characterized in that, The S4 includes the following steps: S41, according to the sampling frequency and the current air volume gear type set, the suction and exhaust noise data of each air volume gear and high frequency in the low-frequency regulated range hood operation process are collected, and a real-time high-frequency suction and exhaust noise data set is obtained; the high-frequency suction and exhaust noise data of multiple future time points is predicted according to the real-time high-frequency suction and exhaust noise data set, and a future high-frequency suction and exhaust noise data set is obtained; S42, if there is high-frequency suction and exhaust noise data greater than or equal to the high-frequency suction and exhaust noise threshold value corresponding to the air volume gear in the high-frequency suction and exhaust noise threshold value set in the real-time high-frequency suction and exhaust noise data set and the future high-frequency suction and exhaust noise data set, S5 is executed; otherwise, the low-frequency regulated range hood is taken as a final range hood.

8. The SVPWM space vector control based suction and exhaust noise regulation method according to claim 7, characterized in that, The S5 includes the following steps: S51, collect the pipe bending curvature data, pipe diameter data and average pipe air volume data of the low-frequency adjusted range hood; S52, input the data collected in S51 into the final high-frequency suction and exhaust noise mapping model for mapping to obtain current high-frequency suction and exhaust noise data; S53, set a maximum repetition number; when the current high-frequency suction and exhaust noise data is greater than or equal to the average value of the high-frequency suction and exhaust noise threshold set, repeat the adjustment of the pipe bending curvature data and pipe diameter data collected in S51, and input the adjusted pipe bending curvature data, pipe diameter data and average pipe air volume data into the final high-frequency suction and exhaust noise mapping model for mapping to obtain current adjusted high-frequency suction and exhaust noise data; when the repetition number is less than or equal to the maximum repetition number and the current adjusted high-frequency suction and exhaust noise data is less than the high-frequency suction and exhaust noise threshold corresponding to the air volume gear in the high-frequency suction and exhaust noise threshold set, the adjustment is completed to obtain a final range hood; otherwise, a macro-control range hood is obtained, and S6 is entered.

9. The SVPWM space vector control based suction and exhaust noise regulation method according to claim 8, characterized in that, The S6 includes the following steps: S61, analyze the motor current signal of the macro-control range hood by FFT to identify the harmonic dominant torque pulsation frequency band and obtain current torque pulsation harmonics; then measure the corresponding aerodynamic noise of the macro-control range hood by using a sound array microphone, extract the standing wave modal frequency, and obtain the current sound modal frequency; S62, establish a coupling matrix of the torque pulsation harmonics and the sound modal frequency, and mark the coincident frequency band in the coupling matrix as a high-risk resonance area; set multiple groups of random frequency ratios and SVPWM vector action time distribution ratios to obtain a historical random frequency ratio set and a historical SVPWM vector action time distribution ratio set; S63, introduce the random frequency of the historical random frequency ratio and combine the historical SVPWM vector action time distribution ratio in the SVPWM carrier cycle corresponding to the macro-control range hood; then collect the sum of the number of high-frequency suction and exhaust noise data greater than or equal to the high-frequency suction and exhaust noise threshold corresponding to the air volume gear in the high-frequency suction and exhaust noise threshold set in the corresponding high-risk resonance area proportion data, real-time high-frequency suction and exhaust noise data and corresponding future time, to obtain a macro-control high-frequency noise number set and current high-risk resonance area proportion data; According to the historical random frequency ratio set, the historical SVPWM vector action time distribution ratio set, the macro-control high-frequency noise number set and the current high-risk resonance area proportion data, a high-frequency noise number mapping model and a high-risk resonance area proportion mapping model are respectively constructed; S64, adjust the current random frequency ratio and the SVPWM vector action time distribution ratio of the macro-control range hood, and input the adjusted data into the high-frequency noise number mapping model and the high-risk resonance area proportion mapping model respectively for mapping to obtain the current high-frequency noise number and the current high-risk resonance area proportion; S65, when the current high-frequency noise number is not 0 or the current high-risk resonance area proportion is not 0, repeat S64 until the current high-frequency noise number and the current high-risk resonance area proportion are both 0, and obtain the final range hood.

10. A system for implementing the method for controlling suction and exhaust noise based on SVPWM space vector control according to any one of claims 1-9, characterized in that it comprises: It comprises a low-frequency suction and exhaust noise data collection module, a low-frequency suction and exhaust noise data determination module, a low-frequency suction and exhaust noise suppression module, a high-frequency suction and exhaust noise data collection and determination module, a range hood pipeline parameter adjustment module, and an SVPWM space vector adjustment module. The low-frequency suction and exhaust noise data collection module collects suction and exhaust noise data at multiple air volume positions and low frequencies during the operation of the range hood to be controlled. A plurality of groups of pipeline bending radius and diameter data, average pipeline air volume data, and corresponding high-frequency suction and exhaust noise data of existing range hoods are collected to construct a final high-frequency suction and exhaust noise mapping model. If the low-frequency suction and exhaust noise data collected by the low-frequency suction and exhaust noise data collection module and the corresponding future time suction and exhaust noise data contain low-frequency suction and exhaust noise data greater than or equal to the corresponding threshold, the low-frequency suction and exhaust noise suppression module is entered. Otherwise, the range hood to be controlled is taken as a low-frequency adjusted range hood, and the high-frequency suction and exhaust noise data collection and determination module is executed. The low-frequency suction and exhaust noise suppression module reinforces and dampens each mechanical component of the range hood to be controlled until the real-time low-frequency suction and exhaust noise data and the corresponding future time data both meet the corresponding threshold, obtaining a low-frequency adjusted range hood and executing the high-frequency suction and exhaust noise data collection and determination module. The high-frequency suction and exhaust noise data collection and determination module collects suction and exhaust noise data at multiple air volume positions and high frequencies during the operation of the low-frequency adjusted range hood. If the collected suction and exhaust noise data and the corresponding future time suction and exhaust noise data contain high-frequency suction and exhaust noise data greater than or equal to the corresponding threshold, the range hood pipeline parameter adjustment module is executed. Otherwise, the low-frequency adjusted range hood is taken as the final range hood. The range hood pipeline parameter adjustment module adjusts the pipeline parameters of the low-frequency adjusted range hood in cooperation with the final high-frequency suction and exhaust noise mapping model and inputs them into the final high-frequency suction and exhaust noise mapping model for mapping. If the mapping result meets the corresponding threshold, the final range hood is obtained. Otherwise, a macro-control range hood is obtained. The SVPWM space vector adjustment module adjusts the current random frequency proportion and SVPWM vector action time allocation proportion of the macro-control range hood until the high-risk resonance area proportion and the high-frequency noise number meet the conditions, and the final range hood is obtained.

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