Method for evaluating subjective noise reduction effect of photovoltaic sound barrier

Abstract

The invention discloses a method for evaluating a subjective noise reduction effect of a photovoltaic sound barrier. The method comprises the following steps: measuring, collecting and analyzing a sound sample before noise control; establishing a sound barrier prediction model, and calculating sound barrier insertion loss; synthesizing a noise-controlled sound sample; according to the sound sample before noise control, the sound sample after noise control and the sound barrier prediction model, calculating or measuring to obtain related parameters; and calculating the reduction percentage of the annoyance degree, and evaluating the subjective noise reduction effect of the photovoltaic sound barrier. Through the steps of collecting a sound sample before noise control, establishing a sound barrier prediction model, synthesizing a sound sample after noise control and the like, the annoyance degree reduction percentage calculated based on a sound absorption coefficient and psychoacoustic parameters is obtained, subjective experiments are not needed in the whole evaluation process, time and labor are saved, operation is easy and convenient, and evaluation is rapid and accurate.

Description

Technical Field

[0001] This invention relates to the field of noise reduction effect evaluation technology, and more specifically, to a method for evaluating the subjective noise reduction effect of a photovoltaic sound barrier. Background Technology

[0002] Solar photovoltaic sound barriers combine photovoltaic cells with sound barriers, effectively controlling noise and possessing the dual functions of noise reduction and photovoltaic power generation.

[0003] Traditional photovoltaic (PV) sound barriers typically employ a method of mounting photovoltaic panels on the back of a sound-absorbing panel to reduce noise. While these barriers offer strong sound insulation, their sound absorption is relatively poor, resulting in significant low-frequency diffraction, particularly affecting the sound quality of the sound shadow zone at the rear of the barrier. Therefore, the sound absorption performance of PV sound barriers is crucial for noise reduction, and a method for evaluating their noise reduction performance based on sound absorption is needed.

[0004] However, existing methods for evaluating the noise reduction performance of photovoltaic (PV) sound barriers mainly rely on objective parameters such as insertion loss, which cannot represent the subjective noise reduction effect of PV sound barriers. Furthermore, the subjective noise reduction effect evaluation method for PV sound barriers is based on the change in the subjective perception of noise quality at sensitive points before and after installation, which requires subjective experiments and is quite cumbersome. Therefore, there is an urgent need for a method that can accurately assess the subjective noise reduction effect of PV sound barriers using key objective parameters. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the purpose of this invention is to provide a method for evaluating the subjective noise reduction effect of photovoltaic sound barriers, and to solve the problem of not making full use of key objective parameters such as sound absorption performance to correctly and quickly evaluate the subjective noise reduction effect of photovoltaic sound barriers.

[0006] To achieve the above objectives, embodiments of the present invention provide a method for evaluating the subjective noise reduction effect of a photovoltaic sound barrier, comprising the following steps: Collect noise control front sample data, obtain its octave spectrum based on the noise control front sample data, establish a sound barrier prediction model and calculate the sound barrier insertion loss; Based on the octave spectrum of the sound sample before noise control and the insertion loss of the sound barrier, the octave spectrum of the sound sample after noise control is calculated. Octave band synthesized noise-controlled acoustic samples based on noise-controlled acoustic samples. After calculating or measuring the relevant parameters of the pre-noise control sound sample, the post-noise control sound sample, and the sound barrier prediction model, the percentage reduction in annoyance level is calculated to evaluate the subjective noise reduction effect of the photovoltaic sound barrier.

[0007] In a preferred embodiment, the acquisition of noise control pre-sound sample data specifically involves: Select several sensitive points, use professional acoustic measurement instruments to measure the A-weighted sound pressure level of the noise on site, and use sound signal acquisition equipment to collect the noise signal. The method for obtaining the octave band spectrum based on noise-controlled front acoustic sample data is as follows: The collected noise signals were imported into acoustic software to calculate the 1 / 3 octave band spectrum of the noise control front sample.

[0008] In a preferred embodiment, the step of establishing a sound barrier prediction model based on noise control front sample data specifically involves: A sound barrier prediction model is established based on the surrounding environment of the sensitive points and various parameters of the sound barrier. The calculation of the sound barrier insertion loss specifically involves: Based on the sound barrier prediction model, the sound barrier insertion loss is calculated using the sound barrier insertion loss calculation formula.

[0009] In a preferred embodiment, the synthesized noise-controlled acoustic sample specifically comprises: The 1 / 3 octave band spectrum of the sound sample before noise control and the calculated insertion loss of the sound barrier are used to calculate the 1 / 3 octave band spectrum of the sound sample after noise control. Then, synthesize the noise-controlled sound sample by using the 1 / 3 octave band spectrum of the obtained noise-controlled sound sample.

[0010] In a preferred embodiment, the calculation of the 1 / 3 octave band spectrum of the noise-controlled acoustic sample specifically involves: Maintaining the correspondence between frequency bands, the control pre-spectrum of the noise control pre-sound sample is... The controlled spectrum of the acoustic sample after noise control is calculated by subtracting the sound pressure level of the sound barrier (IL) from the corresponding frequency band. ,Right now: = -IL.

[0011] In a preferred embodiment, the process of synthesizing the noise-controlled sound sample based on the obtained 1 / 3 octave band spectrum of the noise-controlled sound sample specifically involves: importing the collected sound signal of the noise-controlled sound sample into audio editing software, adjusting the sound pressure level of each frequency band of the noise-controlled sound sample strictly according to the calculated 1 / 3 octave band spectrum of the noise-controlled sound sample, and then synthesizing and exporting the noise-controlled sound sample.

[0012] In a preferred embodiment, the calculation of the annoyance reduction percentage is based on the sound absorption coefficient and psychoacoustic parameters, and the formula for calculating the annoyance reduction percentage (ARP) is as follows:

[0013] in: ARP stands for Annoyance Reduction Percentage, which is used to represent the subjective noise reduction effect of photovoltaic sound barriers. This indicates the low-frequency sound absorption coefficient of the sound barrier, which affects low-frequency diffraction. This indicates the tone scheduling of the noise control front sample; This indicates the sound scheduling of the sound sample after noise control. , , The coefficients for each component are determined through experiments or model calibration, and their range of values ​​is strictly limited as follows: The value range is 0.099 to 0.161; The value range is 0.132 to 0.280; The value range is -0.437 to -0.263.

[0014] In a preferred embodiment, the evaluation method is suitable for evaluating the noise reduction effect of sound-absorbing vertical photovoltaic sound barriers.

[0015] The beneficial effects of this invention application: (1) The formula for calculating the percentage reduction in annoyance is based on the sound absorption coefficient and psychoacoustic parameters. It fully considers the influence of low-frequency diffraction and sound absorption performance on the noise reduction effect of the sound barrier. At the same time, the introduction of psychoacoustic parameters makes up for the shortcomings of using only objective parameters to evaluate the subjective noise reduction effect, so that the percentage reduction in annoyance can more accurately and precisely evaluate the subjective noise reduction effect of the sound barrier. (2) By collecting noise control pre-sound samples, establishing a sound barrier prediction model, and synthesizing noise control post-sound samples, relevant parameters are calculated and measured. Then, the relevant parameters are used to calculate the annoyance percentage. The whole process does not require subjective experiments, saving time and effort, and is simple to operate and quick to evaluate. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the subjective noise reduction effect evaluation method for photovoltaic sound barriers. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figure 1 A method for evaluating the subjective noise reduction effect of photovoltaic sound barriers includes the following steps: S1. Measure, collect and analyze noise control front sound samples; When measuring and collecting sound samples, the specific steps are as follows: select several sensitive points, use professional acoustic measuring instruments to measure the A-weighted sound pressure level of the noise on site, and use sound signal acquisition equipment to collect noise control front sound sample data to obtain the noise signal; when analyzing the noise control front sound sample, the specific steps are as follows: obtain its octave spectrum based on the noise control front sound sample data, that is, import the collected noise sound signal into acoustic software and calculate the 1 / 3 octave spectrum of the noise control front sound sample.

[0019] In one embodiment, to measure, collect, and analyze the noise control pre-sound samples, several sensitive points are selected, primarily noise-sensitive areas such as residential buildings. The measurements are taken in strict accordance with standards such as HJ / T 90-2004 "Acoustic Design and Measurement Specifications for Sound Barriers." Professional acoustic measurement equipment used includes sound level meters; specifically, a BSWA308 multi-functional sound level meter with a tripod can be used. For acquiring sound signals, a Haide Acoustics four-channel portable digital acquisition system, the SQobold, can be used to collect noise signals. After acquiring the noise signals, they can be imported into acoustic software such as Artemis, allowing for the rapid calculation of the 1 / 3 octave band spectrum of the noise control pre-sound samples.

[0020] S2. Establish a sound barrier prediction model and calculate the sound barrier insertion loss; When establishing a sound barrier prediction model, the specific steps are as follows: In accordance with standards such as "HJ / T 90-2004 Sound Barrier Acoustic Design and Measurement Specification", a sound barrier prediction model is established based on the surrounding environment of sensitive points and various parameters of the sound barrier. When calculating the sound barrier insertion loss, the specific steps are as follows: Based on the sound barrier prediction model, the sound barrier insertion loss is calculated using the sound barrier insertion loss calculation formula.

[0021] In one embodiment, to establish a sound barrier prediction model, the sound barrier insertion loss calculation formula, commonly used in practical engineering and given in the standard "HJ / T 90-2004 Sound Barrier Acoustic Design and Measurement Specification," can be used to calculate the sound barrier insertion loss based on the sound barrier prediction model. The sound barrier insertion loss calculation formula is as follows:

[0022] IL: Insertion loss of sound barrier; A-weighted diffraction attenuation of sound barriers; : Transmitted sound correction amount; : Correction amount for reflected sound; Sound attenuation due to obstacles; Ground absorption attenuation.

[0023] The insertion loss IL of a sound barrier depends on: diffraction attenuation, transmitted sound correction, reflected sound reduction, ground absorption attenuation, and obstacle attenuation. Obstacle attenuation refers to the sound attenuation caused by other obstacles between the sound source and the receiving point. When calculating this formula, diffraction attenuation is the primary focus. Three calculation cases are considered: a point sound source, an infinitely long line source with an infinitely long sound barrier, and an infinitely long line source with a finite-length sound barrier. In the case of calculating diffraction attenuation for a point sound source, when the Fresnel number N > 0:

[0024] In the formula, N is the Fresnel number. ; λ is the wavelength of the sound wave (unit: m); d is the straight-line distance between the sound source and the receiving point (unit: m); A is the distance from the sound source to the top of the sound barrier (unit: m). B is the distance from the sound receiving point to the top of the sound barrier (unit: m).

[0025] If there is an angle β between the line connecting the sound source and the receiving point and the normal to the sound barrier, then the Fresnel number should be corrected to: .

[0026] For example, in one embodiment, a sound wave with a speed of sound in air of approximately 343 m / s and a frequency of 1000 Hz has a wavelength of 0.343 meters. The sound barrier is 3 m high, the sound source is 0.5 m above the ground and 10 m away from the sound barrier, the receiving point is 0.5 m above the ground and 20 m away from the sound barrier, and the ground is rigid, so ground absorption is not considered. The corresponding data can then be substituted into the diffraction attenuation calculation formula for sound barrier A-weighted to calculate the diffraction attenuation of sound barrier A. Then, the diffracted sound weighted by the sound barrier A is attenuated. Substituting the values ​​into the formula for calculating the insertion loss of a sound barrier, the insertion loss IL of the sound barrier can then be calculated.

[0027] S3, Sound sample after synthesized noise control; S31. Calculate the 1 / 3 octave band spectrum of the noise control pre-amplitude sample based on the noise control pre-amplitude spectrum in step S1 and the sound barrier insertion loss in step S2; specifically, step S31 involves maintaining the correspondence between frequency bands and calculating the control pre-amplitude spectrum of the noise control pre-amplitude sample. The controlled spectrum of the acoustic sample after noise control is calculated by subtracting the sound pressure level of the sound barrier (IL) from the corresponding frequency band. ,Right now: = -IL.

[0028] S32. Synthesize the noise-controlled sound sample according to the 1 / 3 octave band spectrum of the obtained noise-controlled sound sample.

[0029] Specifically, step S32 involves importing the collected acoustic signal of the noise control pre-sound sample into audio editing software, adjusting the sound pressure level of each frequency band of the noise control pre-sound sample according to the calculated 1 / 3 octave band spectrum of the noise control post-sound sample, and then synthesizing and exporting the noise control post-sound sample.

[0030] In step S32, the audio editing software used can be Adobe Audition software. After importing the sound signal of the noise control pre-sound sample, the software's "Effects" → "Filtering and Equalization" → "Graphic Equalizer" function is used to adjust the sound pressure level of each frequency band of the noise control pre-sound sample strictly according to the calculated 1 / 3 octave band spectrum of the noise control post-sound sample, thereby synthesizing the noise control post-sound sample.

[0031] S4. Based on the noise control pre-sound sample, noise control post-sound sample, and sound barrier prediction model, calculate or measure the relevant parameters of the noise control pre-sound sample, noise control post-sound sample, and sound barrier prediction model; then calculate the percentage reduction in annoyance level to evaluate the subjective noise reduction effect of the photovoltaic sound barrier.

[0032] Based on the sound absorption coefficient and psychoacoustic parameters, the formula for calculating the ARP (Anoyance Reduction Ratio) is as follows:

[0033] in: ARP stands for Annoyance Reduction Percentage, which is used to represent the subjective noise reduction effect of photovoltaic sound barriers. This indicates the low-frequency sound absorption coefficient of the sound barrier, which affects low-frequency diffraction. This indicates the tone scheduling of the noise control front sample; This indicates the sound scheduling of the sound sample after noise control. , , The coefficients for each component are determined through experiments or model calibration, and their range of values ​​is strictly limited as follows: The value range is 0.099 to 0.161; The value range is 0.132 to 0.280; The value range is -0.437 to -0.263.

[0034] In one embodiment, the low-frequency sound absorption coefficient of the sound barrier is calculated or measured based on the noise control pre-sound sample, the noise control post-sound sample, and the sound barrier prediction model. Noise control pre-sound sample tone scheduling Sound scheduling of sound samples after noise control And the coefficients of each component are determined by a sound barrier prediction model. , , Then, substituting these values ​​into the formula for calculating the ARP (Average Reduction in Annoyance Level), the ARP is calculated. The lower the ARP value, the worse the subjective noise reduction effect of the photovoltaic sound barrier; the higher the ARP value, the better the subjective noise reduction effect of the photovoltaic sound barrier.

[0035] In summary, by collecting pre-noise control samples, establishing a sound barrier prediction model, and synthesizing post-noise control samples, the percentage reduction in annoyance level, calculated based on the sound absorption coefficient and psychoacoustic parameters, is obtained. Using this percentage reduction in annoyance level to evaluate the subjective noise reduction effect of photovoltaic sound barriers is particularly suitable for assessing the noise reduction effect of sound-absorbing vertical photovoltaic sound barriers. The entire evaluation process requires no subjective experiments, saving time and effort, and is simple to operate, providing rapid and accurate evaluation. Furthermore, the formula for calculating the percentage reduction in annoyance level fully considers the influence of low-frequency diffraction and sound absorption performance on the noise reduction effect of the sound barrier, effectively compensating for the shortcomings of using only objective parameters to evaluate subjective noise reduction effects. This makes the percentage reduction in annoyance level a more accurate and precise assessment of the subjective noise reduction effect of the sound barrier.

[0036] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0037] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0038] Those skilled in the art will recognize that the modules 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.

[0039] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0040] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0041] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the subjective noise reduction effect of a photovoltaic sound barrier, characterized in that, Includes the following steps: Collect noise control front sample data, obtain its octave spectrum based on the noise control front sample data, establish a sound barrier prediction model and calculate the sound barrier insertion loss; Based on the octave spectrum of the sound sample before noise control and the insertion loss of the sound barrier, the octave spectrum of the sound sample after noise control is calculated. Octave band synthesized noise-controlled acoustic samples based on noise-controlled acoustic samples. After calculating or measuring the relevant parameters of the pre-noise control sound sample, the post-noise control sound sample, and the sound barrier prediction model, the percentage reduction in annoyance level is calculated to evaluate the subjective noise reduction effect of the photovoltaic sound barrier.

2. The method for evaluating the subjective noise reduction effect of the photovoltaic sound barrier according to claim 1, characterized in that, Specifically, when collecting noise control pre-sound sample data, the following steps are taken: Select several sensitive points, use professional acoustic measurement instruments to measure the A-weighted sound pressure level of the noise on site, and use sound signal acquisition equipment to collect the noise signal. The method for obtaining the octave band spectrum based on noise-controlled front acoustic sample data is as follows: The collected noise signals were imported into acoustic software to calculate the 1 / 3 octave band spectrum of the noise control front sample.

3. The method for evaluating the subjective noise reduction effect of the photovoltaic sound barrier according to claim 1, characterized in that, Specifically, when establishing a sound barrier prediction model based on noise control front sample data, the following is done: A sound barrier prediction model is established based on the surrounding environment of the sensitive points and various parameters of the sound barrier. The calculation of the sound barrier insertion loss specifically involves: Based on the sound barrier prediction model, the sound barrier insertion loss is calculated using the sound barrier insertion loss calculation formula.

4. The method for evaluating the subjective noise reduction effect of the photovoltaic sound barrier according to claim 1, characterized in that, The synthesized noise-controlled acoustic sample is specifically: The 1 / 3 octave band spectrum of the sound sample before noise control and the calculated insertion loss of the sound barrier are used to calculate the 1 / 3 octave band spectrum of the sound sample after noise control. Then, synthesize the noise-controlled sound sample by using the 1 / 3 octave band spectrum of the obtained noise-controlled sound sample.

5. The method for evaluating the subjective noise reduction effect of the photovoltaic sound barrier according to claim 4, characterized in that, Specifically, when calculating the 1 / 3 octave band spectrum of the noise-controlled acoustic sample, the following steps are taken: Maintaining the correspondence between frequency bands, the control pre-spectrum of the noise control pre-sound sample is... The controlled spectrum of the acoustic sample after noise control is calculated by subtracting the sound pressure level of the sound barrier (IL) from the corresponding frequency band. ,Right now: = -IL.

6. The method for evaluating the subjective noise reduction effect of the photovoltaic sound barrier according to claim 4, characterized in that, When synthesizing the noise-controlled sound sample based on the obtained 1 / 3 octave band spectrum of the noise-controlled sound sample, the specific steps are as follows: the sound signal of the collected noise-controlled sound sample before the noise control is imported into the audio editing software, so that it strictly adjusts the sound pressure level of each frequency band of the noise-controlled sound sample according to the calculated 1 / 3 octave band spectrum of the noise-controlled sound sample, and then synthesizes and exports the noise-controlled sound sample.

7. The method for evaluating the subjective noise reduction effect of the photovoltaic sound barrier according to claim 1, characterized in that, The calculation of the percentage reduction in annoyance level (ARP) is based on the sound absorption coefficient and psychoacoustic parameters, and the formula for calculating the percentage reduction in annoyance level (ARP) is as follows: in: ARP stands for Annoyance Reduction Percentage, which is used to represent the subjective noise reduction effect of photovoltaic sound barriers. This indicates the low-frequency sound absorption coefficient of the sound barrier, which affects low-frequency diffraction. This indicates the tone scheduling of the noise control front sample; This indicates the sound scheduling of the sound sample after noise control. , , The coefficients for each component are determined through experiments or model calibration, and their range of values ​​is strictly limited as follows: The value range is 0.099 to 0.161; The value range is 0.132 to 0.280; The value range is -0.437 to -0.

263.

8. The method for evaluating the subjective noise reduction effect of the photovoltaic sound barrier according to any one of claims 1-7, characterized in that, The evaluation method described herein is applicable to evaluating the noise reduction effect of sound-absorbing vertical photovoltaic sound barriers.

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