A method and system for analyzing noise reduction effects based on soundproofing materials

Abstract

The application relates to the technical field of material science, and discloses a noise reduction effect analysis method and system based on sound insulation materials, which comprises the following steps: collecting environmental noise of a sound insulation material in a target environment according to the material characteristics and the frequency range of the sound insulation material; detecting sound pressure level data of the environmental noise; extracting frequency domain features and time domain features of the sound pressure level data, and generating a noise simulation signal corresponding to the target environment according to the frequency domain features and the time domain features; performing noise reduction simulation on the noise simulation signal through dynamic time points and sound insulation material parameters to obtain a noise reduction simulation signal; calculating a noise reduction score of the noise reduction simulation signal by using a noise reduction effect scoring function; and determining the noise reduction effect of the sound insulation material according to the noise reduction score. The application can improve the accuracy of noise reduction effect analysis of the sound insulation material.

Description

Technical Field

[0001] This invention relates to the field of materials science and technology, and in particular to a method and system for analyzing the noise reduction effect of sound insulation materials. Background Technology

[0002] In many places, such as homes, offices, and schools, people hope to enjoy a relatively quiet and comfortable environment. However, long-term exposure to high-intensity noise environments can have a negative impact on people's health and quality of life. Therefore, measures need to be taken to reduce the transmission of external noise and provide good sound insulation. To achieve sound insulation, sound insulation materials can be used to block, absorb, or reflect sound waves to reduce the transmission of external noise. However, to improve the sound insulation effect of sound insulation materials, it is necessary to analyze the noise reduction effect of the sound insulation materials.

[0003] Existing techniques for analyzing the noise reduction effect of sound insulation materials involve testing in specialized soundproofing laboratories. These laboratories use acoustic testing equipment to simulate sound propagation in a real environment and measure the material's sound insulation performance at different frequencies, thus evaluating its noise reduction effectiveness. However, in practical applications, testing in soundproofing laboratories requires manual control to simulate sound propagation in a real environment, leading to inaccurate noise reduction analysis results and consequently low precision in analyzing the noise reduction effect of sound insulation materials. Summary of the Invention

[0004] This invention provides a method and system for analyzing the noise reduction effect of sound insulation materials, the main purpose of which is to solve the problem of low accuracy in analyzing the noise reduction effect of sound insulation materials.

[0005] To achieve the above objectives, the present invention provides a method for analyzing the noise reduction effect based on sound insulation materials, comprising:

[0006] S1. Obtain the material properties of the sound insulation material, and collect the environmental noise of the sound insulation material in a preset target environment based on the material properties and a preset frequency range;

[0007] S2. Detect the sound pressure level data of the ambient noise using a preset microphone array and a preset dynamic measurement combination;

[0008] S3. Extract the frequency domain features of the sound pressure level data, extract the time domain features of the sound pressure level data, and generate a noise simulation signal corresponding to the target environment based on the frequency domain features and the time domain features;

[0009] S4. The noise simulation signal is subjected to noise reduction simulation using preset dynamic time points and preset sound insulation material parameters to obtain a noise reduction simulation signal;

[0010] S5. Calculate the noise reduction score of the noise reduction analog signal using a preset noise reduction effect scoring function, and determine the noise reduction effect of the sound insulation material based on the noise reduction score. The calculation of the noise reduction score of the noise reduction analog signal using the preset noise reduction effect scoring function includes:

[0011] S51. Extract the signal parameters of the noise-reduced analog signal;

[0012] S52. Calculate the noise reduction score of the noise-reduced analog signal based on the signal parameters and the material parameters of the sound insulation material using the noise reduction effect scoring function described above:

[0013]

[0014] Wherein, S is the noise reduction score, γ1 is the sound transmission loss factor, A1 is the incident sound pressure in the signal parameters, A2 is the transmitted sound pressure in the signal parameters, γ2 is the sound absorption factor, R is the reflection coefficient in the signal parameters, T is the transmission coefficient in the signal parameters, γ3 is the equivalent sound absorption factor, D is the material surface area in the material parameters, α is the sound absorption coefficient, γ4 is the sound propagation attenuation factor, d is the sound propagation distance in the signal parameters, d0 is the preset sound propagation reference distance, and log is the logarithmic function.

[0015] Optionally, the step of collecting the environmental noise of the sound insulation material in a preset target environment based on the material properties and a preset frequency range includes:

[0016] The noise sampling location in the target environment is determined based on the material properties.

[0017] Detect the single-point sound signal at the noise acquisition location according to the frequency range;

[0018] The individual sound signals are fused according to a preset acquisition sequence to obtain the ambient noise in the target environment.

[0019] Optionally, the step of detecting the sound pressure level data of the ambient noise through a preset microphone array and a preset dynamic measurement combination includes:

[0020] The microphone array is dynamically adjusted according to the dynamic measurement combination to obtain the array layout of the microphone array;

[0021] Each microphone in the array layout is synchronized and calibrated to obtain a synchronized array layout.

[0022] Collect the output signal of each microphone in the synchronous array layout;

[0023] The output signal is converted into sound pressure level data of the ambient noise.

[0024] Optionally, converting the output signal into sound pressure level data of the ambient noise includes:

[0025] Convert the output signal into a signal energy value;

[0026] The signal calibration factor for each microphone in each synchronization array layout is calculated based on the signal value of the output signal and the preset calibration signal.

[0027] The sound pressure level of the ambient noise is calculated based on the signal energy value and the signal calibration factor, wherein the sound pressure level calculation formula is:

[0028]

[0029] Where L is the sound pressure level, log is the logarithmic function, P is the signal calibration factor, and Q is the signal energy value;

[0030] The sound pressure level data of each microphone in the synchronous array layout is integrated to obtain the sound pressure level data of the ambient noise.

[0031] Optionally, extracting the frequency domain features of the sound pressure level data includes:

[0032] The sound pressure level data is converted into frequency domain data using a preset fast Fourier transform algorithm;

[0033] Determine the amplitude spectrum of the sound pressure level data based on the frequency domain data;

[0034] The power spectrum and energy spectrum of the sound pressure level data are determined based on the amplitude spectrum.

[0035] The amplitude of the sound pressure level data is extracted based on the amplitude spectrum, the power of the sound pressure level data is extracted based on the power spectrum, and the energy of the sound pressure level data is extracted based on the energy spectrum.

[0036] The amplitude, power, and energy are fused into the frequency domain characteristics of the sound pressure level data.

[0037] Optionally, the extraction of the time-domain features of the sound pressure level data includes:

[0038] The sound pressure level data is used to generate a sound pressure waveform.

[0039] Extract the peak sound pressure level from the sound pressure waveform;

[0040] Calculate the average sound pressure and root mean square sound pressure based on the sound pressure level data in the sound pressure waveform diagram;

[0041] The peak sound pressure level, the average sound pressure level, and the root mean square sound pressure level are fused together to form the time-domain characteristics of the sound pressure level data.

[0042] Optionally, generating the noise simulation signal corresponding to the target environment based on the frequency domain features and the time domain features includes:

[0043] A noise signal model is determined based on the frequency domain characteristics and the time domain characteristics;

[0044] Noise parameters are generated based on the frequency domain features and the time domain features;

[0045] The noise signal is obtained by adjusting the noise signal model using the noise parameters.

[0046] The noise signal is matched with the noise signal of the target environment to obtain a matched signal;

[0047] When the matched signal meets the preset noise signal requirements, the matched signal is used as the noise simulation signal corresponding to the target environment.

[0048] Optionally, the step of performing noise reduction simulation on the noise simulation signal using preset dynamic time points and preset sound insulation material parameters to obtain a noise reduction simulation signal includes:

[0049] The simulation time interval of the noise simulation signal is configured by the dynamic time points;

[0050] Extract the acoustic transmission characteristics from the parameters of the sound insulation material;

[0051] Within the simulation time interval, the transmission factor of the noise simulation signal is determined based on the acoustic transmission characteristics;

[0052] An acoustic transmission signal is generated based on the transmission factor, and the noise simulation signal is adjusted based on the acoustic transmission signal to obtain a noise-reduced simulation signal.

[0053] Optionally, determining the noise reduction effect of the sound insulation material based on the noise reduction score includes:

[0054] The objective evaluation value of the sound insulation material is determined based on the noise reduction score.

[0055] Obtain the subjective evaluation value of the sound insulation material;

[0056] A combined evaluation value for the sound insulation material is generated based on the objective evaluation value and the subjective evaluation value, wherein the formula for calculating the combined evaluation value is:

[0057]

[0058] Wherein, H is the combined evaluation value. To evaluate the optimization factors, λ1 is the objective evaluation weight, λ2 is the subjective evaluation weight, E is the objective evaluation value, and F is the subjective evaluation value.

[0059] The sound insulation level of the sound insulation material is determined based on the combined evaluation value;

[0060] The noise reduction effect of the sound insulation material is determined based on the sound insulation level.

[0061] To address the aforementioned problems, the present invention also provides a noise reduction effect analysis system based on sound insulation materials, the system comprising:

[0062] An environmental noise acquisition module is used to acquire the material properties of the sound insulation material and, based on the material properties and a preset frequency range, acquire the environmental noise of the sound insulation material in a preset target environment.

[0063] The sound pressure level data monitoring module is used to detect the sound pressure level data of the ambient noise through a preset microphone array and a preset dynamic measurement combination;

[0064] The noise simulation signal generation module is used to extract the frequency domain features of the sound pressure level data, extract the time domain features of the sound pressure level data, and generate a noise simulation signal corresponding to the target environment based on the frequency domain features and the time domain features.

[0065] The noise reduction simulation signal generation module is used to perform noise reduction simulation on the noise simulation signal using preset dynamic time points and preset sound insulation material parameters to obtain a noise reduction simulation signal.

[0066] The noise reduction effect analysis module is used to calculate the noise reduction score of the noise reduction analog signal using a preset noise reduction effect scoring function, and determine the noise reduction effect of the sound insulation material based on the noise reduction score.

[0067] This invention collects environmental noise data of the sound insulation material in a target environment, thereby selecting the most suitable sound insulation material to achieve the best sound insulation effect. Detecting the sound pressure level data of the environmental noise helps to understand the spatial distribution characteristics of the noise in the target environment, aiding in determining the location and propagation patterns of the noise source. Extracting the frequency and time domain features of the sound pressure level data, and generating a noise simulation signal corresponding to the target environment based on these features, allows for a better understanding and response to noise conditions in real-world environments. Performing noise reduction simulation on the noise simulation signal allows observation of the impact of different parameters on the noise reduction effect, thus selecting the most suitable sound insulation material. Calculating the noise reduction score of the noise reduction simulation signal, and determining the noise reduction effect of the sound insulation material based on the score, provides an objective and standardized evaluation method, avoiding interference from subjective factors. By calculating the noise reduction score, the noise reduction effect of the sound insulation material can be evaluated more accurately. Therefore, the noise reduction effect analysis method and system based on sound insulation materials proposed in this invention can solve the problem of low accuracy in analyzing the noise reduction effect of sound insulation materials. Attached Figure Description

[0068] Figure 1 This is a flowchart illustrating a method for analyzing the noise reduction effect based on sound insulation materials according to an embodiment of the present invention.

[0069] Figure 2 This is a schematic diagram of the process for detecting sound pressure level data according to an embodiment of the present invention;

[0070] Figure 3 This is a schematic diagram of the process for generating a noise analog signal according to an embodiment of the present invention;

[0071] Figure 4 This is a functional block diagram of a noise reduction effect analysis system based on sound insulation materials provided in an embodiment of the present invention;

[0072] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0073] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0074] This application provides a method for analyzing the noise reduction effect based on sound insulation materials. The execution subject of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for analyzing the noise reduction effect based on sound insulation materials can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0075] Reference Figure 1 The diagram shown is a flowchart illustrating a noise reduction effect analysis method based on sound insulation materials according to an embodiment of the present invention. In this embodiment, the noise reduction effect analysis method based on sound insulation materials includes:

[0076] S1. Obtain the material properties of the sound insulation material, and collect the environmental noise of the sound insulation material in a preset target environment based on the material properties and a preset frequency range.

[0077] In this embodiment of the invention, the material properties include the thickness, layers, density, bending stiffness, and durability of the sound insulation material. The material properties of the sound insulation material can be obtained from a pre-stored storage area using computer statements with data capture capabilities (such as Java statements, Python statements, etc.).

[0078] In detail, the thickness of the material has a certain impact on the sound insulation effect; thicker materials can provide better sound insulation; the greater the density, the better the sound insulation; the greater the degree of bending, the better the material's effect on the propagation of sound waves and vibration; and the better the durability, the longer the sound insulation effect can be maintained.

[0079] Furthermore, it is necessary to collect environmental noise in a specific environment based on the material properties and the preset frequency range. This allows us to understand the sound absorption effect of the sound insulation material within a specific frequency range, and thus select the most suitable sound insulation material in the target environment to achieve the best sound insulation effect.

[0080] In this embodiment of the invention, the environmental noise is sound pollution generated in the target environment that may have a negative impact on human health and comfort, such as noise from traffic, factories, construction sites, machinery and equipment, etc., which can be reduced by sound insulation materials to reduce the distribution and propagation of noise.

[0081] In this embodiment of the invention, the step of collecting the environmental noise of the sound insulation material in a preset target environment based on the material properties and a preset frequency range includes:

[0082] The noise sampling location in the target environment is determined based on the material properties.

[0083] Detect the single-point sound signal at the noise acquisition location according to the frequency range;

[0084] The individual sound signals are fused according to a preset acquisition sequence to obtain the ambient noise in the target environment.

[0085] In detail, the target environment includes, but is not limited to, rooms, buildings, and road areas. First, the source of noise that may be generated is determined. Based on the material properties of the sound insulation material, the area in the target environment where noise needs to be collected is determined, thereby determining the noise collection location. For example, if the material is thin, a noise collection location is set up in the thinner area; or if the material has a low density, a noise collection location is set up in the lower density area. Then, at each noise collection location, the individual sound signal at each noise collection location is recorded by the noise collection device according to the preset frequency range, that is, the single-point sound signal of the noise collection location. Then, the single-point sound signals are spliced ​​and fused according to the order of collecting noise collection locations to obtain the overall environmental noise in the target environment.

[0086] For example, if the single-point sound signal at noise acquisition location ① is A, the single-point sound signal at noise acquisition location ② is B, and the single-point sound signal at noise acquisition location ③ is C, then the single-point sound signals corresponding to different noise acquisition locations are spliced ​​and fused together to obtain the environmental noise in the target environment as {A,B,C}, that is, the different single-point sound signals are combined and gathered together to form the environmental noise in the target environment.

[0087] Furthermore, data analysis and signal processing of environmental noise in the target environment are required. By using a microphone array, the spatial distribution of noise can be captured, thereby obtaining sound pressure level data at different locations. This helps to understand the spatial distribution characteristics of noise in the target environment and helps to determine the location and propagation patterns of noise sources.

[0088] S2. The sound pressure level data of the ambient noise is detected by a preset microphone array and a preset dynamic measurement combination.

[0089] In this embodiment of the invention, the sound pressure level data is a physical quantity that describes the sound intensity. By detecting the corresponding sound pressure level data in the environmental noise, further data analysis and processing can be performed, such as spectrum analysis and noise source identification, to provide a scientific basis for formulating effective noise control strategies.

[0090] In this embodiment of the invention, reference is made to Figure 2 As shown, the detection of the sound pressure level data of the ambient noise through a preset microphone array and a preset dynamic measurement combination includes:

[0091] S21. Dynamically adjust the microphone array according to the dynamic measurement combination to obtain the array layout of the microphone array;

[0092] S22. Perform synchronous calibration on each microphone in the array layout to obtain a synchronous array layout;

[0093] S23. Acquire the output signal of each microphone in the synchronous array layout;

[0094] S24. Convert the output signal into sound pressure level data of the ambient noise.

[0095] In detail, the dynamic measurement combination refers to the position and orientation of the microphones. By configuring different positions and orientations of each microphone, different microphone arrays can be formed, which can comprehensively detect the sound pressure level data of ambient noise. Then, by adjusting the position and orientation of each microphone in the microphone array according to the dynamic measurement combination of the microphones, different array layouts of microphone arrays can be obtained to capture the most accurate sound field data possible.

[0096] Specifically, the frequency response and sensitivity of each microphone need to be calibrated to ensure that the output signals of all microphones have similar responses. In dynamic measurements, it is also necessary to ensure that the microphones in the microphone array are time-synchronized, ensuring that the sampling time is consistent between different microphones, avoiding data confusion and distortion. Calibration and synchronization equipment is used to ensure the accuracy and synchronization of the microphone output signals. By providing a precise reference signal and clock synchronization mechanism, the consistency and accuracy of the data can be ensured, thus obtaining a synchronized array layout. The output signals of each microphone in the synchronized array layout are then collected by sensors and converted into sound pressure level data of ambient noise, which facilitates accurate analysis of subsequent data.

[0097] In this embodiment of the invention, converting the output signal into sound pressure level data of the ambient noise includes:

[0098] Convert the output signal into a signal energy value;

[0099] The signal calibration factor for each microphone in each synchronization array layout is calculated based on the signal value of the output signal and the preset calibration signal.

[0100] The sound pressure level of the ambient noise is calculated based on the signal energy value and the signal calibration factor, wherein the sound pressure level calculation formula is:

[0101]

[0102] Where L is the sound pressure level, log is the logarithmic function, P is the signal calibration factor, and Q is the signal energy value;

[0103] The sound pressure level data of each microphone in the synchronous array layout is integrated to obtain the sound pressure level data of the ambient noise.

[0104] In detail, a Fourier transform is performed on the output signal to obtain its spectral information. The energy value of the signal can be obtained by calculating the squared amplitude of each frequency component in the spectrum and summing the results. The Fourier transform formula is as follows: The frequency domain data is X[k], the discrete time domain signal is x[n], N represents the number of sampling points of the signal, k represents the spectrum index, and j is the imaginary unit. It describes how the signal divides the continuous frequency into N discrete frequency components in the frequency domain. The amplitude and phase of each frequency component are determined by the corresponding time domain signal x[n].

[0105] Specifically, the output signal value of each microphone is compared with a preset calibration reference signal. The signal difference between the output signal value and the calibration reference signal value is calculated, and this signal difference is used as the signal calibration factor for each microphone. Then, the sound pressure level data of the ambient noise is calculated based on the signal energy value and the signal calibration factor. The signal calibration factor is a compensation coefficient used to correct the frequency response and sensitivity differences of each microphone. In order to correct the sensitivity and frequency response differences between different microphones, the sound pressure level data corresponding to each microphone are integrated together to obtain the overall sound pressure level distribution of the ambient noise. Thus, the sound pressure level data of the ambient noise is obtained. It is also necessary to calibrate and normalize the converted sound pressure level data to ensure that they have a consistent reference level.

[0106] Furthermore, the combination of microphone array and dynamic measurement enables multi-point acquisition, thereby covering different areas and locations of the target environment and obtaining more comprehensive sound pressure level data. This helps to fully understand the characteristics of environmental noise, and the noise of the target environment can be analyzed based on the data characteristics of the sound pressure level data, so as to better and more accurately analyze the noise reduction effect of sound insulation materials.

[0107] S3. Extract the frequency domain features of the sound pressure level data, extract the time domain features of the sound pressure level data, and generate a noise simulation signal corresponding to the target environment based on the frequency domain features and the time domain features.

[0108] In this embodiment of the invention, the frequency domain features refer to the characteristics and attributes of the signal in the frequency domain. By performing Fourier transform or other frequency domain analysis methods on the signal, the energy distribution and spectrum information of the signal at different frequencies can be obtained.

[0109] In this embodiment of the invention, extracting the frequency domain features of the sound pressure level data includes:

[0110] The sound pressure level data is converted into frequency domain data using a preset fast Fourier transform algorithm;

[0111] Determine the amplitude spectrum of the sound pressure level data based on the frequency domain data;

[0112] The power spectrum and energy spectrum of the sound pressure level data are determined based on the amplitude spectrum.

[0113] The amplitude of the sound pressure level data is extracted based on the amplitude spectrum, the power of the sound pressure level data is extracted based on the power spectrum, and the energy of the sound pressure level data is extracted based on the energy spectrum.

[0114] The amplitude, power, and energy are fused into the frequency domain characteristics of the sound pressure level data.

[0115] In detail, the sampling rate of the audio signal needs to be determined first. The sampling rate represents how many sample data are collected per second and is a basic parameter after the audio signal is digitized. Then, the length of the Fast Fourier Transform (FFT) algorithm needs to be selected. Usually, the length should be chosen to be the closest integer power of 2 to make full use of the properties of the FFT algorithm and reduce the amount of computation. In order to avoid spectral leakage (i.e., the energy of each frequency component is mixed together and difficult to distinguish), the audio signal needs to be windowed. Window functions include Hamming window and Heining window. The windowed audio signal is then input into the FFT algorithm to perform the FFT and obtain the frequency domain data.

[0116] Specifically, taking the absolute value of the frequency domain data obtained by the Fast Fourier Transform (FFT) algorithm yields the amplitude spectrum, which displays the amplitude of each frequency component. The energy of each frequency component can be calculated from the amplitude spectrum by squaring the amplitude of each component and summing them up to obtain the total energy of the entire signal. The power spectrum is the square of the energy of each frequency component, which can be calculated from the amplitude spectrum by squaring the amplitude of each component and then averaging it by dividing it by the total signal length (or FFT length) to obtain the power of each frequency component. The energy spectrum is the distribution of energy of each frequency component, which can be calculated from the amplitude spectrum by squaring the amplitude of each component and then normalizing it by dividing it by the total signal energy to obtain the energy distribution of each frequency component. The amplitude in the amplitude spectrum, the power in the power spectrum, and the energy in the energy spectrum are then fused into the frequency domain characteristics of the sound pressure level data. When the amplitude is A, the power is B, and the energy is C, the frequency domain characteristics are {A, B, C}.

[0117] Furthermore, frequency domain features refer to the characteristics and attributes of a signal in the frequency domain. They can reflect the energy distribution and spectral information of sound at different frequencies. Extracting frequency domain features allows for a more comprehensive analysis of the characteristics and attributes of sound, improving the accuracy and efficiency of sound processing and recognition.

[0118] In this embodiment of the invention, the time-domain characteristics refer to the characteristics and attributes of the signal in the time domain. For example, the mean of the signal can reflect the offset of the signal on the time axis, and the variance of the signal can reflect the volatility and degree of change of the signal. The larger the variance, the more violent the signal fluctuation.

[0119] In this embodiment of the invention, extracting the temporal features of the sound pressure level data includes:

[0120] The sound pressure level data is used to generate a sound pressure waveform.

[0121] Extract the peak sound pressure level from the sound pressure waveform;

[0122] Calculate the average sound pressure and root mean square sound pressure based on the sound pressure level data in the sound pressure waveform diagram;

[0123] The peak sound pressure level, the average sound pressure level, and the root mean square sound pressure level are fused together to form the time-domain characteristics of the sound pressure level data.

[0124] In detail, a sound pressure level (SPL) waveform is generated for each microphone, where the horizontal axis represents the microphone's serial number and the vertical axis represents the corresponding SPL data. The maximum peak value of the SPL data is then extracted from the waveform, which describes the maximum sound intensity. The average SPL and root mean square (RMS) of the SPL data are calculated from the waveform. The average SPL is the sum of all values ​​divided by the number of data points, reflecting the overall SPL level. The RMS is the square root of the average of the squares of all data points, reflecting the energy of the sound signal. Furthermore, the peak-to-peak value of the SPL data, the difference between the maximum and minimum values, can be calculated, reflecting the dynamic range of the sound signal.

[0125] Specifically, sound pressure level data can also be plotted as a time-domain waveform. The time-domain waveform shows the change of sound pressure level over time, which can capture the instantaneous characteristics, duration, and periodicity of the sound signal. By plotting the time series data as the horizontal axis and the sound pressure level data as the vertical axis, the waveform is plotted to ensure that the sound pressure level value of each sampling point is correctly mapped to the corresponding time scale. The time scale corresponding to the sound pressure level data is determined. The time scale depends on the sampling rate of the data acquisition, that is, the number of sampling points per second. The time interval can be calculated based on the sampling rate.

[0126] Furthermore, by using the peak sound pressure level (PSL), average sound pressure level (APS), and root mean square (RMS) as the time-domain features of the SPL data, such as PPL a, average sound pressure level b, and RMS c, the time-domain features of the SPL data are {a, b, c}. This allows for time-domain analysis of the SPL data, enabling an understanding of the characteristics of the sound signal from both a holistic and local perspective. This can reveal the temporal characteristics and dynamic properties of the SPL data.

[0127] In this embodiment of the invention, the noise simulation signal is a signal used to simulate noise conditions in a real environment. The noise simulation signal can better understand and respond to noise conditions in a real environment.

[0128] In this embodiment of the invention, reference is made to Figure 3 As shown, generating the noise simulation signal corresponding to the target environment based on the frequency domain features and the time domain features includes:

[0129] S31. Determine the noise signal model based on the frequency domain characteristics and the time domain characteristics;

[0130] S32. Generate noise parameters based on the frequency domain characteristics and the time domain characteristics;

[0131] S33. Adjust the noise signal model using the noise parameters to obtain the noise signal;

[0132] S34. Match the noise signal with the noise signal of the target environment to obtain a matching signal;

[0133] S35. When the matching signal meets the preset noise signal requirements, the matching signal is used as the noise simulation signal corresponding to the target environment.

[0134] Specifically, the noise signal models include Gaussian white noise, additive noise, exponential noise, and colored noise. Gaussian white noise is the most common noise model, exhibiting a uniform distribution in the frequency domain and being stationary in the time domain. It can be generated using a random number generator to produce a Gaussian-distributed random number sequence, which is then added to the signal to be simulated. Additive noise is an analog signal obtained by superimposing the noise signal onto the original signal. The noise signal can be considered as an independent signal generated by various noise sources, and then added to the original signal. Exponential noise is a non-stationary noise whose power increases or decreases exponentially with time. Noise signals with exponential characteristics are generated using exponential functions or stochastic processes. Colored noise refers to noise signals with non-uniform spectral distribution. Common colored noises include red noise (strong low-frequency components), blue noise (strong high-frequency components), and pink noise (flat spectrum). The noise signal model is then determined based on frequency and time domain characteristics. For example, features in the frequency domain such as power spectral density, spectral shape, and bandwidth, and statistical features in the time domain, can be used to determine Gaussian white noise. Gaussian white noise has a uniformly distributed power spectral density, exhibits stationary characteristics in the time domain, and has a random waveform, making it suitable for simulating various random noises. If the noise signal in the target environment has a constant power spectral density, a Gaussian white noise model can be chosen; if the noise signal has strong energy in the low-frequency band, a Brownian noise model can be chosen. The choice depends on the noise characteristics of the target environment and the selected noise model.

[0135] Specifically, the noise parameters include mean, variance, amplitude, and frequency. Extracting data from frequency domain features and time domain features determines the noise parameters. Frequency domain features include amplitude, energy, and power, while time domain features include mean, variance, and peak value. These noise parameters are then used to adjust the parameters of the noise signal model. For example, Gaussian white noise is a common noise model that can be described by two parameters: mean and variance. The mean controls the DC component of the noise signal, and the variance controls the intensity of the noise signal. Increasing the mean will cause the noise signal to shift upwards in the time domain, while decreasing the mean will cause it to shift downwards. The average level of the noise signal can be adjusted by changing the value of the mean. Increasing the variance will affect the amplitude range of the noise signal. Increasing the variance means the noise is stronger; decreasing the variance reduces the amplitude range of the noise signal, meaning the noise is weaker. The intensity of the noise signal can be adjusted by changing the variance value, thus obtaining an adjusted noise signal that better matches the characteristics of the target environment. Noise signal data can be generated using software tools such as MATLAB, or real-time simulation can be performed using hardware devices. After generating the noise signal, it needs to be verified to ensure that the generated simulated signal matches the noise signal of the target environment. Verification methods include, but are not limited to, visualization and analysis. Only when the matched signal meets the signal requirements of the real environment is this matched signal used as the noise simulation signal consistent with the target environment.

[0136] Furthermore, by designing a suitable noise model based on the time-domain and frequency-domain characteristics of the target environment, noise signals in the target environment can be effectively simulated and applied in multiple fields such as acoustic research, noise control, and audio processing.

[0137] S4. The noise simulation signal is subjected to noise reduction simulation using preset dynamic time points and preset sound insulation material parameters to obtain a noise reduction simulation signal.

[0138] In this embodiment of the invention, the noise reduction simulation signal refers to the evaluation of the performance of the sound insulation material by simulating noise reduction. By adjusting the parameters of the sound insulation material, the influence of different parameters on the noise reduction effect can be observed, thereby selecting the most suitable sound insulation material.

[0139] In this embodiment of the invention, the step of performing noise reduction simulation on the noise simulation signal using preset dynamic time points and preset sound insulation material parameters to obtain a noise reduction simulation signal includes:

[0140] The simulation time interval of the noise simulation signal is configured by the dynamic time points;

[0141] Extract the acoustic transmission characteristics from the parameters of the sound insulation material;

[0142] Within the simulation time interval, the transmission factor of the noise simulation signal is determined based on the acoustic transmission characteristics;

[0143] An acoustic transmission signal is generated based on the transmission factor, and the noise simulation signal is adjusted based on the acoustic transmission signal to obtain a noise-reduced simulation signal.

[0144] In detail, the dynamic time points refer to different times. The activity cycle of the noise source is accurately simulated using dynamic time points. The dynamic time points can be determined based on the activity patterns of the noise source to define the simulation time interval. If the noise source only generates noise within a specific time period each day, then by configuring corresponding dynamic time points, the simulation time interval can be limited to this time period to more accurately simulate the actual situation. The acoustic transmission characteristics include absorption coefficient, reflection coefficient, transmission coefficient, sound velocity, and density. The absorption coefficient, reflection coefficient, and transmission coefficient of a material can be measured through acoustic experiments, while the sound velocity and density of the material can be obtained through calculation and simulation. The absorption coefficient refers to the material's ability to absorb sound energy; a higher absorption coefficient indicates a stronger absorption capacity for sound waves. Sound insulation refers to the material's ability to isolate sound waves; a higher sound insulation indicates a better isolation effect. The reflection coefficient is the proportion of incident sound wave energy reflected by the material, representing the proportion of sound waves reflected from the material surface. The transmission coefficient is the proportion of incident sound wave energy transmitted by the material.

[0145] Specifically, the transmission factor refers to the propagation process of sound waves on the sound insulation material, including effects such as sound wave attenuation, reflection, and transmission. An acoustic transmission signal is then generated based on the transmission factor; that is, the noise simulation signal is simulated by sound insulation treatment based on the transmission factor to obtain the acoustic transmission signal. The amplitude, frequency domain, or time domain of the acoustic transmission signal is then adjusted accordingly. After the sound insulation treatment simulation, the resulting signal is the noise reduction simulation signal. Based on the parameters of the sound insulation material and the acoustic transmission law, numerical calculation methods or analytical methods can be used to simulate the propagation process of the noise signal on the sound insulation material, and the signal can be processed accordingly to obtain the noise reduction simulation signal. Commonly used numerical calculation methods include the finite element method, boundary element method, and finite difference method. By discretizing the sound field and the sound insulation material model, numerical solution techniques can be used to calculate the propagation process of the noise signal on the sound insulation material. Numerical calculation methods can consider complex acoustic transmission characteristics and the non-uniformity of the sound insulation material, providing more accurate simulation results.

[0146] Furthermore, in order to analyze the sound insulation effect of sound insulation materials, it is necessary to evaluate it based on the noise reduction simulation signal after sound insulation, so as to accurately analyze the sound insulation effect of sound insulation materials.

[0147] S5. Calculate the noise reduction score of the noise reduction analog signal using a preset noise reduction effect scoring function, and determine the noise reduction effect of the sound insulation material based on the noise reduction score.

[0148] In this embodiment of the invention, the noise reduction score refers to the evaluation value after simulating the noise reduction of the sound insulation material in the target environment, thereby evaluating the noise reduction effect of the sound insulation material based on the noise reduction score.

[0149] In this embodiment of the invention, calculating the noise reduction score of the noise-reduced analog signal using a preset noise reduction effect scoring function includes:

[0150] Extract the signal parameters of the noise-reduced analog signal;

[0151] The noise reduction score of the simulated signal is calculated using the following noise reduction performance scoring function based on the signal parameters and the material parameters of the sound insulation material:

[0152]

[0153] Wherein, S is the noise reduction score, γ1 is the sound transmission loss factor, A1 is the incident sound pressure in the signal parameters, A2 is the transmitted sound pressure in the signal parameters, γ2 is the sound absorption factor, R is the reflection coefficient in the signal parameters, T is the transmission coefficient in the signal parameters, γ3 is the equivalent sound absorption factor, D is the material surface area in the material parameters, α is the sound absorption coefficient, γ4 is the sound propagation attenuation factor, d is the sound propagation distance in the signal parameters, d0 is the preset sound propagation reference distance, and log is the logarithmic function.

[0154] In detail, the signal parameters include incident sound pressure, transmitted sound pressure, reflection coefficient, transmission coefficient, and sound propagation distance in the noise-reduced analog signal. The signal parameters of the noise-reduced analog signal can be extracted from a pre-stored storage area using computer statements with data capture capabilities (such as Java statements, Python statements, etc.). The storage area includes, but is not limited to, databases and blockchains.

[0155] Specifically, the material parameters of the sound insulation material include its surface area. These parameters can be scored based on the applied noise simulation signal of the sound insulation material using a noise reduction effect scoring function. In this function, the sound transmission loss factor γ1 adjusts the weight of the transmitted sound pressure ratio, the sound absorption factor γ2 adjusts the weight of the reflection coefficient and transmission coefficient, the equivalent sound absorption factor γ3 adjusts the weight of the sound absorption coefficient, and the sound propagation attenuation factor γ4 adjusts the weight of the sound propagation distance. The sound absorption coefficient α = 1 - RT. Therefore, the sound transmission loss of the sound insulation material at different frequencies is calculated as 10log[…]. 10 A1 / A2, the degree of sound wave absorption 1-RT, the sound absorption capacity of the material D×α, and the attenuation of sound during propagation 20log 10 d / d0 can be used to analyze and evaluate the noise reduction effect of sound insulation materials.

[0156] Furthermore, by using a preset noise reduction performance scoring function to calculate the noise reduction score of the noise-reduced analog signal, an objective and standardized evaluation method can be provided, thereby avoiding the interference of subjective factors. By calculating the noise reduction score, the noise reduction effect of sound insulation materials can be evaluated more accurately.

[0157] In this embodiment of the invention, the noise reduction effect generally refers to the ability to reduce or eliminate noise and disturbances in the environment.

[0158] In this embodiment of the invention, determining the noise reduction effect of the sound insulation material based on the noise reduction score includes:

[0159] The objective evaluation value of the sound insulation material is determined based on the noise reduction score.

[0160] Obtain the subjective evaluation value of the sound insulation material;

[0161] A combined evaluation value for the sound insulation material is generated based on the objective evaluation value and the subjective evaluation value, wherein the formula for calculating the combined evaluation value is:

[0162]

[0163] Wherein, H is the combined evaluation value. To evaluate the optimization factors, λ1 is the objective evaluation weight, λ2 is the subjective evaluation weight, E is the objective evaluation value, and F is the subjective evaluation value.

[0164] The sound insulation level of the sound insulation material is determined based on the combined evaluation value;

[0165] The noise reduction effect of the sound insulation material is determined based on the sound insulation level.

[0166] In detail, the objective evaluation value E is calculated using the parameters of the noise-reduced analog signal. The subjective evaluation value is the user's subjective feeling and evaluation of the sound insulation material, which can be obtained through user surveys, questionnaires, or feedback on actual use. The objective evaluation weight λ1 and the subjective evaluation weight λ2 are weighted and considered together to determine the objective evaluation value E and the subjective evaluation value F, and an evaluation optimization factor is added. Calculate the combined evaluation value H of the sound insulation materials, where the evaluation optimization factor is... This refers to the situation where subjective and objective evaluation values ​​deviate from the actual situation, or where the errors between subjective and objective evaluation values ​​are significant, in which case an evaluation optimization factor is applied. Assigning a moderate rating to the combined evaluation value can comprehensively assess the noise reduction effect of sound insulation materials.

[0167] Specifically, based on the combined evaluation value H, the sound insulation level of the sound insulation material can be determined according to preset evaluation standards or rules. The determined sound insulation level corresponds to a specific noise reduction effect; a higher sound insulation level indicates a better noise reduction effect. For example, a higher combined evaluation value corresponds to a higher sound insulation level. This allows for analysis of the sound insulation effect of the material, and the noise reduction score can serve as feedback information to help manufacturers or designers improve the material design and manufacturing process. By analyzing the influence of various parameters in the noise reduction score, the composition and structure of the material can be optimized to improve the noise reduction effect.

[0168] This invention collects environmental noise data of the sound insulation material in a target environment, thereby selecting the most suitable sound insulation material to achieve the best sound insulation effect. Detecting the sound pressure level data of the environmental noise helps to understand the spatial distribution characteristics of the noise in the target environment, aiding in determining the location and propagation patterns of the noise source. Extracting the frequency and time domain features of the sound pressure level data, and generating a noise simulation signal corresponding to the target environment based on these features, allows for a better understanding and response to noise conditions in real-world environments. Performing noise reduction simulation on the noise simulation signal allows observation of the impact of different parameters on the noise reduction effect, thus selecting the most suitable sound insulation material. Calculating the noise reduction score of the noise reduction simulation signal, and determining the noise reduction effect of the sound insulation material based on the score, provides an objective and standardized evaluation method, avoiding interference from subjective factors. By calculating the noise reduction score, the noise reduction effect of the sound insulation material can be evaluated more accurately. Therefore, the noise reduction effect analysis method and system based on sound insulation materials proposed in this invention can solve the problem of low accuracy in analyzing the noise reduction effect of sound insulation materials.

[0169] like Figure 4 The diagram shown is a functional block diagram of a noise reduction effect analysis system based on sound insulation materials provided in an embodiment of the present invention.

[0170] The noise reduction effect analysis system 100 based on sound insulation materials described in this invention can be installed in electronic devices. Depending on the functions implemented, the noise reduction effect analysis system 100 may include an environmental noise acquisition module 101, a sound pressure level data monitoring module 102, a noise simulation signal generation module 103, a noise reduction simulation signal generation module 104, and a noise reduction effect analysis module 105. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0171] In this embodiment, the functions of each module / unit are as follows:

[0172] The environmental noise acquisition module 101 is used to acquire the material properties of the sound insulation material and acquire the environmental noise of the sound insulation material in a preset target environment according to the material properties and a preset frequency range.

[0173] The sound pressure level data monitoring module 102 is used to detect the sound pressure level data of the ambient noise through a preset microphone array and a preset dynamic measurement combination.

[0174] The noise simulation signal generation module 103 is used to extract the frequency domain features of the sound pressure level data, extract the time domain features of the sound pressure level data, and generate a noise simulation signal corresponding to the target environment based on the frequency domain features and the time domain features.

[0175] The noise reduction simulation signal generation module 104 is used to perform noise reduction simulation on the noise simulation signal through preset dynamic time points and preset sound insulation material parameters to obtain a noise reduction simulation signal.

[0176] The noise reduction effect analysis module 105 is used to calculate the noise reduction score of the noise reduction analog signal using a preset noise reduction effect scoring function, and determine the noise reduction effect of the sound insulation material based on the noise reduction score.

[0177] In detail, the modules in the noise reduction effect analysis system 100 based on sound insulation materials described in this embodiment of the invention employ the same methods as described above during use. Figures 1 to 3 The method used is the same as the noise reduction effect analysis method based on sound insulation materials described in the article, and can produce the same technical effect, so it will not be repeated here.

[0178] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0179] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0180] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0181] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0182] Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects. The scope of the invention is not limited to the foregoing description, and all variations within the meaning and scope of equivalents falling within the protection scope are intended to be included in the invention.

[0183] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0184] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems described in the system embodiments may also be implemented by a single unit or system through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for analyzing the noise reduction effect based on sound insulation materials, characterized in that, The method includes: S1. Obtain the material properties of the sound insulation material, and collect the environmental noise of the sound insulation material in a preset target environment based on the material properties and a preset frequency range; S2. Detect the sound pressure level data of the ambient noise using a preset microphone array and a preset dynamic measurement combination; S3. Extract the frequency domain features of the sound pressure level data, extract the time domain features of the sound pressure level data, and generate a noise simulation signal corresponding to the target environment based on the frequency domain features and the time domain features; S4. The noise simulation signal is subjected to noise reduction simulation using preset dynamic time points and preset sound insulation material parameters to obtain a noise reduction simulation signal; S5. Calculate the noise reduction score of the noise reduction analog signal using a preset noise reduction effect scoring function, and determine the noise reduction effect of the sound insulation material based on the noise reduction score. The calculation of the noise reduction score of the noise reduction analog signal using the preset noise reduction effect scoring function includes: S51. Extract the signal parameters of the noise-reduced analog signal; S52. Calculate the noise reduction score of the noise-reduced analog signal based on the signal parameters and the material parameters of the sound insulation material using the noise reduction effect scoring function described above: Wherein, S is the noise reduction score, γ1 is the sound transmission loss factor, A1 is the incident sound pressure in the signal parameters, A2 is the transmitted sound pressure in the signal parameters, γ2 is the sound absorption factor, R is the reflection coefficient in the signal parameters, T is the transmission coefficient in the signal parameters, γ3 is the equivalent sound absorption factor, D is the material surface area in the material parameters, α is the sound absorption coefficient, γ4 is the sound propagation attenuation factor, d is the sound propagation distance in the signal parameters, d0 is the preset sound propagation reference distance, and log is the logarithmic function.

2. The noise reduction effect analysis method based on sound insulation materials as described in claim 1, characterized in that, The step of collecting environmental noise of the sound insulation material in a preset target environment based on the material properties and a preset frequency range includes: The noise sampling location in the target environment is determined based on the material properties. Detect the single-point sound signal at the noise acquisition location according to the frequency range; The individual sound signals are fused according to a preset acquisition sequence to obtain the ambient noise in the target environment.

3. The noise reduction effect analysis method based on sound insulation materials as described in claim 1, characterized in that, The detection of the sound pressure level data of the ambient noise through a preset microphone array and a preset dynamic measurement combination includes: The microphone array is dynamically adjusted according to the dynamic measurement combination to obtain the array layout of the microphone array; Each microphone in the array layout is synchronized and calibrated to obtain a synchronized array layout. Collect the output signal of each microphone in the synchronous array layout; The output signal is converted into sound pressure level data of the ambient noise.

4. The noise reduction effect analysis method based on sound insulation materials as described in claim 1, characterized in that, The step of converting the output signal into sound pressure level data of the ambient noise includes: Convert the output signal into a signal energy value; The signal calibration factor for each microphone in each synchronization array layout is calculated based on the signal value of the output signal and the preset calibration signal. The sound pressure level of the ambient noise is calculated based on the signal energy value and the signal calibration factor, wherein the sound pressure level calculation formula is: Wherein, L is the sound pressure level, log is the logarithmic function, P is the signal calibration factor, and Q is the signal energy value; The sound pressure level data of each microphone in the synchronous array layout is integrated to obtain the sound pressure level data of the ambient noise.

5. The noise reduction effect analysis method based on sound insulation materials as described in claim 1, characterized in that, The extraction of frequency domain features from the sound pressure level data includes: The sound pressure level data is converted into frequency domain data using a preset fast Fourier transform algorithm; Determine the amplitude spectrum of the sound pressure level data based on the frequency domain data; The power spectrum and energy spectrum of the sound pressure level data are determined based on the amplitude spectrum. The amplitude of the sound pressure level data is extracted based on the amplitude spectrum, the power of the sound pressure level data is extracted based on the power spectrum, and the energy of the sound pressure level data is extracted based on the energy spectrum. The amplitude, power, and energy are fused into the frequency domain characteristics of the sound pressure level data.

6. The noise reduction effect analysis method based on sound insulation materials as described in claim 1, characterized in that, The extraction of the temporal features of the sound pressure level data includes: The sound pressure level data is used to generate a sound pressure waveform. Extract the peak sound pressure level from the sound pressure waveform; Calculate the average sound pressure and root mean square sound pressure based on the sound pressure level data in the sound pressure waveform diagram; The peak sound pressure level, the average sound pressure level, and the root mean square sound pressure level are fused together to form the time-domain characteristics of the sound pressure level data.

7. The noise reduction effect analysis method based on sound insulation materials as described in claim 1, characterized in that, The step of generating the noise simulation signal corresponding to the target environment based on the frequency domain features and the time domain features includes: A noise signal model is determined based on the frequency domain characteristics and the time domain characteristics; Noise parameters are generated based on the frequency domain features and the time domain features; The noise signal is obtained by adjusting the noise signal model using the noise parameters. The noise signal is matched with the noise signal of the target environment to obtain a matched signal; When the matched signal meets the preset noise signal requirements, the matched signal is used as the noise simulation signal corresponding to the target environment.

8. The noise reduction effect analysis method based on sound insulation materials as described in claim 1, characterized in that, The step of performing noise reduction simulation on the noise simulation signal using preset dynamic time points and preset sound insulation material parameters to obtain a noise reduction simulation signal includes: The simulation time interval of the noise simulation signal is configured by the dynamic time points; Extract the acoustic transmission characteristics from the parameters of the sound insulation material; Within the simulation time interval, the transmission factor of the noise simulation signal is determined based on the acoustic transmission characteristics; An acoustic transmission signal is generated based on the transmission factor, and the noise simulation signal is adjusted based on the acoustic transmission signal to obtain a noise-reduced simulation signal.

9. The method for analyzing the noise reduction effect based on sound insulation materials as described in claim 1, characterized in that, Determining the noise reduction effect of the sound insulation material based on the noise reduction score includes: The objective evaluation value of the sound insulation material is determined based on the noise reduction score. Obtain the subjective evaluation value of the sound insulation material; A combined evaluation value for the sound insulation material is generated based on the objective evaluation value and the subjective evaluation value, wherein the formula for calculating the combined evaluation value is: Wherein, H is the combined evaluation value. To evaluate the optimization factors, λ1 is the objective evaluation weight, λ2 is the subjective evaluation weight, E is the objective evaluation value, and F is the subjective evaluation value. The sound insulation level of the sound insulation material is determined based on the combined evaluation value; The noise reduction effect of the sound insulation material is determined based on the sound insulation level.

10. A noise reduction effect analysis system based on sound insulation materials, characterized in that, The system includes: An environmental noise acquisition module is used to acquire the material properties of the sound insulation material and, based on the material properties and a preset frequency range, acquire the environmental noise of the sound insulation material in a preset target environment. The sound pressure level data monitoring module is used to detect the sound pressure level data of the ambient noise through a preset microphone array and a preset dynamic measurement combination; The noise simulation signal generation module is used to extract the frequency domain features of the sound pressure level data, extract the time domain features of the sound pressure level data, and generate a noise simulation signal corresponding to the target environment based on the frequency domain features and the time domain features. The noise reduction simulation signal generation module is used to perform noise reduction simulation on the noise simulation signal using preset dynamic time points and preset sound insulation material parameters to obtain a noise reduction simulation signal. The noise reduction effect analysis module is used to calculate the noise reduction score of the noise reduction analog signal using a preset noise reduction effect scoring function, and determine the noise reduction effect of the sound insulation material based on the noise reduction score. The calculation of the noise reduction score of the noise reduction analog signal using the preset noise reduction effect scoring function includes: Extract the signal parameters of the noise-reduced analog signal; calculate the noise reduction score of the noise-reduced analog signal based on the signal parameters and the material parameters of the sound insulation material using the following noise reduction effect scoring function: Wherein, S is the noise reduction score, γ1 is the sound transmission loss factor, A1 is the incident sound pressure in the signal parameters, A2 is the transmitted sound pressure in the signal parameters, γ2 is the sound absorption factor, R is the reflection coefficient in the signal parameters, T is the transmission coefficient in the signal parameters, γ3 is the equivalent sound absorption factor, D is the material surface area in the material parameters, α is the sound absorption coefficient, γ4 is the sound propagation attenuation factor, d is the sound propagation distance in the signal parameters, d0 is the preset sound propagation reference distance, and log is the logarithmic function.

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