Intelligent screening method and system for raw materials based on sound insulation material

Abstract

The invention relates to the technical field of material analysis, and discloses an intelligent raw material composition screening method and system based on a sound insulation material, and the method comprises the steps: collecting acoustic characteristic test data, microstructure data and chemical composition data of a candidate sound insulation material; performing frequency response curve decomposition on the acoustic characteristic test data to obtain a characteristic frequency band sound absorption coefficient, and constructing an acoustic impedance tensor of the sound insulation material to analyze the sound wave dissipation performance of the candidate sound insulation material; calculating the pore distribution entropy and connectivity index of the candidate sound insulation material to analyze the sound wave propagation retardation efficiency of the candidate sound insulation material; analyzing the molecular chain flexibility and interfacial compatibility of the candidate sound insulation material to evaluate the acoustic performance stability of the candidate sound insulation material for multi-component compounding; and screening a target material from the candidate sound insulation materials by using sound wave dissipation performance, sound wave propagation retardation efficiency and acoustic performance stability. The performance matching degree of the raw materials screened out of the sound insulation material in actual composite use can be improved.

Description

Technical Field

[0001] This invention relates to a method and system for intelligent screening of raw materials for sound insulation materials, belonging to the field of materials analysis technology. Background Technology

[0002] High-performance sound insulation materials have wide applications in architectural acoustics, transportation, and industrial noise control. For example, they are used for impact sound insulation in floor sound insulation systems, broadband noise absorption in aircraft cabins, and acoustic suppression of structural vibrations in high-end equipment. The overall performance of sound insulation materials directly affects the noise comfort, privacy protection, and compliance of the final product; therefore, the scientific and precise selection of their constituent raw materials is crucial.

[0003] Currently, the selection of raw materials for sound insulation materials largely relies on traditional, experience-based methods: initial judgment is made through single-parameter testing, followed by selection of raw materials based on manual experience for small-scale trial production, and finally, the suitability of the raw materials is inferred from the performance of the finished products. However, this method only focuses on individual isolated parameters, ignoring the inherent relationship between the acoustic properties and physical structure of the raw materials, leading to performance mismatches between the selected raw materials and their actual composite applications. Summary of the Invention

[0004] This invention provides a method and system for intelligent screening of raw materials for sound insulation materials. Its main purpose is to improve the performance matching degree of the raw materials screened out for sound insulation materials in actual composite use.

[0005] To achieve the above objectives, the present invention provides a method for intelligent screening of raw materials for sound insulation materials, comprising: Collect acoustic property test data, microstructure data, and chemical composition data of candidate sound insulation materials; The acoustic characteristic test data is decomposed into frequency response curves to obtain the characteristic frequency band sound absorption coefficient. The acoustic impedance tensor of the candidate sound insulation material is constructed using the characteristic frequency band sound absorption coefficient. The sound wave dissipation performance of the candidate sound insulation material is analyzed using the acoustic impedance tensor. Using the microstructure data, the pore distribution entropy and connectivity index of the candidate sound insulation material are calculated. Based on the pore distribution entropy and connectivity index, the sound wave propagation hindrance efficiency of the candidate sound insulation material is analyzed. Based on the chemical composition data, the molecular chain flexibility and interfacial compatibility of the candidate sound insulation materials are analyzed. Using the molecular chain flexibility and interfacial compatibility, the acoustic performance stability of the candidate sound insulation materials in multi-component composites is evaluated. Target materials are selected from the candidate sound insulation materials by utilizing the sound wave dissipation performance, the sound wave propagation hindrance efficiency, and the acoustic performance stability.

[0006] Optionally, based on the pore distribution entropy and the connectivity index, the sound wave propagation blocking efficiency of the candidate sound insulation material is analyzed, including: The intensity of the scattering effect of sound waves in the porous medium of the candidate sound insulation material is analyzed using the pore distribution entropy, and the intensity of the scattering effect is used as the first hindrance factor of the candidate sound insulation material. Using the connectivity index, the tortuosity and reflectivity of the sound wave propagation path in the candidate sound insulation material are analyzed to obtain the second hindrance factor. The overall acoustic wave hindrance coefficient of the candidate sound insulation material is calculated using the first hindrance factor and the second hindrance factor. Based on the comprehensive acoustic wave hindrance coefficient, the acoustic wave propagation hindrance efficiency of the candidate sound insulation material is determined.

[0007] Optionally, calculating the comprehensive acoustic wave hindrance coefficient of the candidate sound insulation material using the first hindrance factor and the second hindrance factor includes: The first blocking factor and the second blocking factor are coupled to obtain a coupling factor; The coupling factor is subjected to multivariate nonlinear regression calculation to identify the acoustic hysteresis coefficient of the candidate sound insulation material using the regression calculation results of the coupling factor. The acoustic wave hindrance coefficient is corrected for acoustic wave propagation confidence to obtain the comprehensive acoustic wave hindrance coefficient.

[0008] Optionally, the acoustic impedance tensor is used to analyze the acoustic dissipation performance of the candidate sound insulation material, including: Based on the acoustic impedance tensor, the acoustic frequency and incident angle of the corresponding sound wave incident on the candidate sound insulation material are identified. Based on the sound wave frequency and the sound wave incident angle, calculate the sound intensity transmission coefficient and sound intensity reflection coefficient of the candidate sound insulation material; The sound energy dissipation rate of the candidate sound insulation material is calculated using the sound intensity transmission coefficient and the sound intensity reflection coefficient. Based on the sound energy dissipation rate, the sound wave dissipation performance of the candidate sound insulation material is analyzed.

[0009] Optionally, the acoustic performance stability of the candidate sound insulation material in a multi-component composite is evaluated by utilizing the flexibility of the molecular chains and the interfacial compatibility, including: Based on the molecular chain flexibility, construct the acoustic performance-flexibility relationship curve of the candidate sound insulation material; Based on the acoustic performance-flexibility relationship curve, the contribution of molecular chain flexibility to the acoustic stability of the candidate sound insulation material is analyzed to obtain the first contribution value; Based on the interface compatibility, construct the acoustic performance-compatibility relationship curve of the candidate sound insulation material; Using the acoustic performance-compatibility relationship curve, the contribution of the interface compatibility to the acoustic stability of the candidate sound insulation material is analyzed to obtain a second contribution value; Based on the first contribution value and the second contribution value, the acoustic performance stability of the candidate sound insulation material after multi-component composite is determined.

[0010] Optionally, using the microstructure data, the pore distribution entropy and connectivity index of the candidate sound insulation material are calculated, including: Using the microstructure data, a binary image of the pore structure of the candidate sound insulation material is obtained. Using the binary image of the pore structure, multiple pore size parameters of the pore structure in the candidate sound insulation material are identified; Based on the multiple pore size parameters, the pore distribution entropy of the candidate sound insulation material is calculated; Using the binary image of the pore structure, the number of connected paths of the candidate sound insulation material is identified, and based on the number of connected paths, the connectivity index of the candidate sound insulation material is calculated.

[0011] Optionally, the acoustic characteristic test data can be decomposed into frequency response curves to obtain the characteristic frequency band absorption coefficient, including: The acoustic property test data are preprocessed to obtain a complete frequency response curve for the candidate sound insulation material; Query the target application scenarios corresponding to the candidate sound insulation materials, and identify the noise spectrum corresponding to the target application scenarios; Based on the noise spectrum, the corresponding curve is truncated from the complete frequency response curve to obtain the target frequency response curve; The sound absorption coefficient of the characteristic frequency band is obtained by integrating the target frequency response curve.

[0012] Optionally, the acoustic impedance tensor of the candidate sound insulation material is constructed using the characteristic frequency band sound absorption coefficient, including: The sound absorption coefficient of the characteristic frequency band is decomposed into low-frequency coefficient, mid-frequency coefficient and high-frequency coefficient to obtain the third-order characteristic frequency band; The third-order feature frequency band is converted into a three-dimensional matrix; Using the three-dimensional matrix, the acoustic impedance tensor of the candidate sound insulation material is constructed.

[0013] Optionally, target materials are screened from the candidate sound insulation materials using the sound wave dissipation performance, the sound wave propagation hindrance efficiency, and the acoustic performance stability, including: Query the actual application scenarios of the candidate sound insulation materials and the corresponding application requirements of the actual application scenarios; Based on the actual application scenario and the application requirements, weights are assigned to the sound wave dissipation performance, sound wave propagation hindrance efficiency and acoustic performance stability of the candidate sound insulation materials to obtain screening indicators. Based on the screening criteria, sound insulation materials that meet the application requirements are selected from the candidate sound insulation materials to obtain the target material.

[0014] To address the above problems, the present invention also provides an intelligent screening system for the constituent raw materials of sound insulation materials, the system comprising: The data acquisition module is used to collect acoustic property test data, microstructure data, and chemical composition data of candidate sound insulation materials; The acoustic performance analysis module is used to decompose the acoustic characteristic test data into frequency response curves to obtain the characteristic frequency band sound absorption coefficient, use the characteristic frequency band sound absorption coefficient to construct the acoustic impedance tensor of the candidate sound insulation material, and use the acoustic impedance tensor to analyze the sound wave dissipation performance of the candidate sound insulation material. The material structure analysis module is used to calculate the pore distribution entropy and connectivity index of the candidate sound insulation material using the microstructure data, and to analyze the sound wave propagation hindrance efficiency of the candidate sound insulation material based on the pore distribution entropy and connectivity index. The chemical performance analysis module is used to analyze the molecular chain flexibility and interfacial compatibility of the candidate sound insulation material based on the chemical composition data, and to evaluate the acoustic performance stability of the candidate sound insulation material when it is multi-component composite using the molecular chain flexibility and interfacial compatibility. The material screening module is used to screen target materials from the candidate sound insulation materials by utilizing the sound wave dissipation performance, the sound wave propagation hindrance efficiency, and the acoustic performance stability.

[0015] This invention first collects acoustic property test data, microstructure data, and chemical composition data of candidate sound insulation materials, constructing a basic database from three key dimensions: "function-structure-essence," providing complete data support for subsequent performance analysis. Next, this invention decomposes the acoustic data into frequency response curves to obtain characteristic frequency band absorption coefficients, extracting the key frequency band sound absorption capabilities that meet actual needs, avoiding full-band analysis from obscuring core performance. Furthermore, it decomposes the absorption coefficients into low, mid, and high-frequency characteristic bands, transforming them into a 3×3 three-dimensional matrix and combining it with material density and sound velocity corrections to quantify the material's ability to impede sound waves of different directions and frequencies. Finally, by identifying the sound wave frequency and incident angle, and combining the sound intensity transmission / reflection coefficient formula (based on impedance matching principles), it calculates the sound energy dissipation rate, accurately determining the material's core ability to "absorb noise" rather than simply "block surface noise." Then, this invention utilizes microscopic data to obtain binary images of the pore structure, identifies pore diameter parameters, and calculates the pore distribution entropy (reflecting pore uniformity) and connectivity index (reflecting pore penetration). Based on these two indices, it analyzes the sound wave propagation hindrance efficiency: the distribution entropy is transformed into a first hindrance factor, and the connectivity index into a second hindrance factor. Then, through factor coupling, nonlinear regression, and confidence correction, a comprehensive hindrance coefficient is obtained. This achieves a precise correlation between microscopic structure and macroscopic sound insulation capability. Furthermore, this invention first quantifies the flexibility of molecular chains by extracting information such as the chemical bond energy of the main chain and the steric hindrance of the side chains (e.g., the plasticizer content in rubber-based materials determines flexibility), constructing an "acoustic performance-flexibility" relationship curve to obtain the first contribution value (quantifying the impact of flexibility on stability); then, it analyzes the polarity of components and the matching degree of functional groups to evaluate interfacial compatibility, constructing an "acoustic performance-compatibility" relationship curve (e.g., the higher the compatibility index under humid heat aging, the lower the attenuation rate), obtaining the second contribution value (quantifying the impact of compatibility); finally, it calculates the comprehensive stability index by weighted summation, thereby evaluating the acoustic performance stability of the multi-component composite and avoiding long-term performance degradation due to mismatch issues in the composite. Furthermore, this invention integrates three core indicators—sound wave dissipation performance, retardation efficiency, and stability—and assigns weights and performs screening based on scenario requirements, avoiding insufficient application adaptability caused by a single indicator. Therefore, this invention can improve the performance matching degree of the selected raw materials for sound insulation materials in actual composite applications. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an embodiment of the intelligent screening method for the constituent raw materials of sound insulation materials provided by the present invention. Figure 2 This is a schematic diagram of a module for implementing an intelligent screening method for the constituent raw materials of sound insulation materials according to an embodiment of the present invention.

[0017] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides an intelligent screening method for the raw materials of sound insulation materials. The executing entity of this intelligent screening 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 intelligent screening method for the raw materials of sound insulation materials can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating an intelligent screening method for raw materials used in sound insulation materials according to an embodiment of the present invention. In this embodiment, the intelligent screening method for raw materials used in sound insulation materials includes: S1. Collect acoustic property test data, microstructure data and chemical composition data of candidate sound insulation materials.

[0021] This invention provides comprehensive data support for the selection of candidate sound insulation materials by collecting acoustic property test data, microstructure data, and chemical composition data. This data covers three key dimensions: sound insulation function, structural support, and essential properties, avoiding the biased judgments caused by traditional screening methods that rely on only a single data point (such as measuring only the sound absorption coefficient). For example, when selecting sound insulation materials for building exterior walls, it is necessary to collect data on their 20-200Hz low-frequency sound absorption coefficient (acoustic data), pore size distribution (microscopic data), and moisture-resistant chemical components (chemical data) to initially determine whether the material meets the low-frequency sound insulation requirements of exterior walls, whether the structure is conducive to blocking sound waves, and whether it can withstand long-term changes in outdoor temperature and humidity.

[0022] The acoustic property test data refers to the quantitative indicators related to the absorption, blocking, and reflection of sound by the candidate sound insulation material, such as the sound absorption coefficient, sound insulation, and acoustic impedance of the material at different frequencies. The microstructure data refers to the structural characteristic parameters of the candidate sound insulation material at the microscopic level, such as pore size and distribution, pore connectivity, particle / fiber arrangement, and interlayer interface structure. These microstructures directly determine the propagation path and loss efficiency of sound waves within the material. The chemical composition data refers to the types, proportions, and related characteristic parameters of the chemical substances contained in the candidate sound insulation material, reflecting the influence of the material's "intrinsic properties" on the durability and applicability of acoustic performance, such as the proportion of basic components (e.g., the ratio of rubber to filler in rubber-based sound insulation materials), the types of functional additives (e.g., whether anti-aging agents and temperature-resistant agents are added), and the chemical stability of the components (e.g., whether it is resistant to moisture and high and low temperatures).

[0023] In practice, a test frequency range can be set using an intelligent acoustic testing system (such as a fully automatic impedance tube) to collect data such as the sound absorption coefficient and sound insulation of candidate materials; an intelligent microscopic imaging platform (such as a fully automatic scanning electron microscope) combined with image recognition algorithms can be used to identify the microscopic regions of the material and analyze and output microscopic structural data such as pore distribution and connectivity; and intelligent chemical analysis equipment (such as a fully automatic infrared spectrometer and elemental analysis system) can be used to collect chemical composition data of the material.

[0024] S2. Decompose the acoustic characteristic test data into frequency response curves to obtain the characteristic frequency band sound absorption coefficient. Use the characteristic frequency band sound absorption coefficient to construct the acoustic impedance tensor of the candidate sound insulation material. Use the acoustic impedance tensor to analyze the sound wave dissipation performance of the candidate sound insulation material.

[0025] This invention, through frequency response curve decomposition of the acoustic characteristic test data, obtains characteristic frequency band absorption coefficients. This allows for precise extraction of characteristic frequency band absorption coefficients that meet actual sound insulation requirements from full-band acoustic data, avoiding the obscuring of key frequency band performance by providing a general analysis across the entire frequency band. For example, when selecting automotive sound insulation materials, the absorption coefficients in the mid-to-high frequency band (200-2000Hz) can be specifically extracted to intuitively determine whether the material is suitable for sound insulation requirements in major noise frequency bands such as engine and tire noise.

[0026] The characteristic frequency band sound absorption coefficient refers to the quantitative index of the sound insulation material's ability to absorb incident sound waves within a specific noise frequency range. The higher the value, the better the material's noise absorption effect in the target frequency band.

[0027] As an embodiment of the present invention, the acoustic characteristic test data is decomposed into frequency response curves to obtain the characteristic frequency band sound absorption coefficient, including: The acoustic property test data are preprocessed to obtain a complete frequency response curve for the candidate sound insulation material; Query the target application scenarios corresponding to the candidate sound insulation materials, and identify the noise spectrum corresponding to the target application scenarios; Based on the noise spectrum, the corresponding curve is truncated from the complete frequency response curve to obtain the target frequency response curve; The sound absorption coefficient of the characteristic frequency band is obtained by integrating the target frequency response curve.

[0028] The complete frequency response curve refers to the continuous acoustic response curve covering the audible frequency range (usually 20-20000Hz) obtained after preprocessing the original acoustic characteristic test data of the candidate sound insulation material. The noise spectrum refers to the characteristic curve of noise energy distribution with frequency obtained after analyzing the noise samples of the target application scenario through Fast Fourier Transform (FFT).

[0029] In practice, wavelet thresholding denoising algorithms (such as hard thresholding, with the threshold set to 1.5 times the standard deviation of the test data) can be used to remove environmental noise interference. Then, cubic spline interpolation is used to complete the missing frequency point data in the 20-20000Hz range (such as completing the missing values ​​in the 150-200Hz range caused by the instrument sampling interval), ultimately generating a continuous, smooth, and complete frequency response curve. By calling the preset "application scenario-noise spectrum database" (containing typical noise data for residential, automotive, and factory scenarios), the target application scenario of the material is matched (such as matching the "bedroom partition wall" scenario). Then, Fast Fourier Transform (FFT) is used to perform spectrum analysis on the noise samples of this scenario to identify the main noise frequency bands (such as identifying the 300-1500Hz range of neighbor conversations as the core frequency band in the bedroom partition wall scenario). Based on the identified core noise frequency band (e.g., 300-1500Hz), a Butterworth bandpass filter (order 4, passband ripple ≤ 1dB) is used to truncate the complete frequency response curve, retaining the absorption coefficient variation curve with frequency within this range and discarding irrelevant frequency band data. The target frequency response curve is then integrated with frequency as the weight, using the formula α = ∫(f・α(f)) df / ∫fdf (where f is the frequency, α(f) is the absorption coefficient function of the target frequency response curve, and the integration range is 300-1500Hz), yielding the weighted average absorption coefficient for this frequency band. For example, the calculated absorption coefficient of the bedroom partition material in the characteristic frequency band of 300-1500Hz is 0.85, directly reflecting its ability to absorb conversation noise.

[0030] Furthermore, by utilizing the sound absorption coefficient of the characteristic frequency band, this embodiment of the invention constructs the acoustic impedance tensor of the candidate sound insulation material, which can accurately quantify the material's ability to impede and dissipate energy from sound waves of different directions and frequencies in key noise frequency bands, avoiding the one-sidedness of relying solely on a single sound absorption coefficient. For example, when screening residential sound insulation materials, using the sound absorption coefficient of the 50-300Hz low-frequency characteristic band to construct a tensor can intuitively reflect the material's ability to block neighbor's footsteps and low-frequency noise from household appliances.

[0031] The acoustic impedance tensor refers to the tensor parameter of the sound insulation material that inherently hinders the incidence, reflection, and transmission of sound waves in different directions.

[0032] As an embodiment of the present invention, the acoustic impedance tensor of the candidate sound insulation material is constructed using the characteristic frequency band sound absorption coefficient, including: The sound absorption coefficient of the characteristic frequency band is decomposed into low-frequency coefficient, mid-frequency coefficient and high-frequency coefficient to obtain the third-order characteristic frequency band; The third-order feature frequency band is converted into a three-dimensional matrix; Using the three-dimensional matrix, the acoustic impedance tensor of the candidate sound insulation material is constructed.

[0033] The third-order characteristic frequency band refers to the set of sound absorption coefficients corresponding to the three independent frequency bands of "low frequency, mid frequency, and high frequency" obtained after segmenting the "characteristic frequency band sound absorption coefficient" obtained in the previous stage based on the noise main frequency distribution of the target application scenario of the candidate sound insulation material.

[0034] In practice, frequency bands can be first divided according to the noise frequency distribution of the target application scenario (e.g., for building interior sound insulation scenarios, low frequency 20-200Hz, mid frequency 200-2000Hz, and high frequency 2000-5000Hz). Then, a 1 / 3 octave bandpass filter is used to extract the sound absorption coefficient of the characteristic frequency band segment by segment, and the average sound absorption coefficient in each frequency band is calculated (e.g., the average of 10 frequency points within 20-200Hz in the low frequency band is calculated), to obtain the low-frequency coefficient, mid-frequency coefficient, and high-frequency coefficient of the corresponding frequency band, i.e., the third-order characteristic frequency band. For example, for bedroom partition wall materials, if its characteristic frequency band is 50-3000Hz, it can be decomposed to obtain a low-frequency coefficient of 0.6 for 50-200Hz, a mid-frequency coefficient of 0.8 for 200-2000Hz, and a high-frequency coefficient of 0.5 for 2000-3000Hz. Based on the three coefficients of the third-order characteristic frequency band, and combined with the anisotropic parameters of the material (such as the difference in warp and weft arrangement of fiber materials), a 3×3 initial matrix is ​​constructed. The diagonal elements are assigned low-frequency, mid-frequency, and high-frequency coefficients, respectively, while the off-diagonal elements are calculated based on the coupling degree of the sound absorption coefficients of adjacent frequency bands. Finally, a three-dimensional matrix reflecting the correlation between frequency bands and directions is obtained. Based on acoustic impedance theory, the three-dimensional matrix is ​​physically corrected. The material density (e.g., ρ=1.2kg / m³) and the speed of sound in air (c=343m / s) are introduced to calculate the impedance reference value (A=ρc=411.6Pa・s / m). The matrix elements are normalized with A, and the symmetry of the matrix is ​​verified through finite element simulation. Finally, an acoustic impedance tensor that conforms to the mathematical properties of a second-order tensor is formed.

[0035] Furthermore, by utilizing the acoustic impedance tensor, this embodiment of the invention analyzes the sound dissipation performance of candidate sound insulation materials, enabling precise determination of the actual sound energy dissipation capability of the candidate materials in key noise frequency bands, rather than solely relying on surface blocking effects. For example, when screening office sound insulation materials, this analysis can clarify whether the material can effectively dissipate sound waves in the 500-2000Hz human voice frequency band, preventing noise reflection and residue in the room.

[0036] The sound wave dissipation performance refers to the ability of sound insulation materials to convert the energy of sound waves incident on the interior into heat energy or other non-sound energy through pore friction, molecular vibration, etc., thereby reducing the reflection and transmission of sound waves. It is the core performance of the material to "absorb noise" rather than "block noise".

[0037] As an embodiment of the present invention, the acoustic impedance tensor is used to analyze the sound wave dissipation performance of the candidate sound insulation material, including: Based on the acoustic impedance tensor, the acoustic frequency and incident angle of the corresponding sound wave incident on the candidate sound insulation material are identified. Based on the sound wave frequency and the sound wave incident angle, calculate the sound intensity transmission coefficient and sound intensity reflection coefficient of the candidate sound insulation material; The sound energy dissipation rate of the candidate sound insulation material is calculated using the sound intensity transmission coefficient and the sound intensity reflection coefficient. Based on the sound energy dissipation rate, the sound wave dissipation performance of the candidate sound insulation material is analyzed.

[0038] Wherein, the sound wave frequency refers to the number of vibrations completed by the sound wave incident on the candidate sound insulation material per unit time; the sound wave incident angle refers to the angle between the propagation direction of the incident sound wave and the "normal" (virtual straight line perpendicular to the surface of the material) of the candidate sound insulation material; the sound intensity transmission coefficient refers to the ratio of the sound intensity reflected by the surface of the candidate sound insulation material (sound energy propagation power per unit area) to the total sound intensity incident on the surface of the material; the sound intensity reflection coefficient refers to the ratio of the sound intensity penetrating the candidate sound insulation material to the total sound intensity incident on the surface of the material; and the sound energy dissipation rate refers to the ratio of the sound energy consumed by the candidate sound insulation material internally through friction, vibration, etc., to the total sound intensity incident on the surface of the material.

[0039] In practice, the dynamic variation characteristics of acoustic impedance tensor elements with frequency can be analyzed (e.g., the frequency corresponding to the peak amplitude of the diagonal elements of the tensor), and the dominant frequency of the sound wave can be extracted by combining Fourier transform (e.g., in the scenario of a car engine compartment, the dominant frequency of 200-1500Hz can be identified); at the same time, based on the coupling degree of the off-diagonal elements of the acoustic impedance tensor (the larger the amplitude of the off-diagonal element, the more significant the influence of the incident angle), the incident angle of the sound wave can be determined by inversion calculation (e.g., based on the off-diagonal elements). The amplitude is used to identify the incident angle range of 30°-60°. When analyzing the sound dissipation performance of the candidate sound insulation materials based on the sound energy dissipation rate, the sound energy dissipation rate at each frequency point within the characteristic frequency band (such as 300-2000Hz in the case of building partitions) can be statistically analyzed, and its weighted average value (the weight is the proportion of noise energy at the corresponding frequency) can be calculated and compared with the scene requirement threshold (such as building sound insulation requiring ≥25%). For example, if a material has an average D=32% in 300-2000Hz, which is higher than the threshold, it indicates that its sound dissipation performance is good and meets the requirements of building partitions; if the average D=18%, the material structure needs to be optimized to improve the dissipation capacity.

[0040] As another embodiment of the present invention, the formula for calculating the sound intensity transmission coefficient is as follows:

[0041] in, denoted by , where represents the sound intensity transmission coefficient, and f represents the sound wave frequency. Indicates the angle of incidence of the sound wave. Represents the acoustic impedance tensor. The characteristic acoustic impedance of air. express The real part, express The real part; The formula for calculating the sound intensity reflection coefficient is as follows:

[0042] in, Indicates the sound intensity reflection coefficient. Represents the acoustic impedance tensor. The characteristic acoustic impedance of air.

[0043] It should be noted that the core of the sound intensity transmission coefficient calculation formula is based on the energy transfer law of sound waves at the interface between air and sound insulation materials. By comprehensively considering the influence of sound wave frequency and incident angle on the acoustic impedance characteristics of the material, and combining the interaction relationship between the acoustic impedance tensor and the real part of the air characteristic acoustic impedance, the proportion of sound wave energy penetrating the material is quantified. Essentially, it reflects the proportion of sound wave energy that can pass through the material at different frequencies and incident angles through the degree of impedance matching (the difference between material impedance and air impedance). Changes in frequency and incident angle will indirectly affect the calculation result of transmitted energy by changing the effective value of the acoustic impedance tensor. The final calculation result is a dimensionless value between 0 and 1, intuitively reflecting the material's "transmission blocking ability" to sound waves. The closer the value is to 0, the better the material's effect in blocking sound wave penetration (e.g., a material with a calculated value of 0.1 at 500Hz and 30° incident angle means that only 10% of the incident sound energy penetrates the material); the closer the value is to 1, the weaker the material's ability to block sound waves, and most of the sound energy can penetrate (e.g., a calculated value of 0.9 means that 90% of the sound energy will pass through the material).

[0044] The formula for calculating the sound intensity reflection coefficient is based on the energy reflection mechanism of sound waves at the interface between two media (air and sound insulation material). It quantifies the proportion of sound energy reflected by the material surface by using the difference between the acoustic impedance tensor and the characteristic acoustic impedance of air. Its core principle is to utilize the degree of impedance mismatch (the difference between the material impedance and the air impedance) to reflect the energy reflection caused by the abrupt change in propagation characteristics at the interface. The greater the impedance difference, the higher the proportion of reflected energy. The calculation result is also a dimensionless value between 0 and 1, directly reflecting the material's "reflection ability" of sound waves. The closer the value is to 1, the stronger the material's ability to reflect sound energy (e.g., a calculated value of 0.8 for a certain material means that 80% of the incident sound energy is reflected back); the closer the value is to 0, the weaker the material's ability to reflect sound energy, with most of the sound energy either penetrating the material or being dissipated internally (e.g., a calculated value of 0.1 means that only 10% of the sound energy is reflected).

[0045] Furthermore, in another embodiment of the present invention, the formula for calculating the sound energy dissipation rate is as follows:

[0046] in, Indicates the sound energy dissipation rate. This represents the sound intensity transmission coefficient. This represents the sound intensity reflection coefficient.

[0047] It should be further explained that the core basis of the sound energy dissipation rate calculation formula is the law of conservation of sound wave energy: when a sound wave is incident on the surface of a sound insulation material, the incident sound energy is distributed in only three forms: sound energy reflected by the material surface, transmitted sound energy penetrating the material, and dissipated sound energy absorbed by the internal structure of the material (such as pores, molecular vibrations, etc.) and converted into heat energy or other forms of energy. Therefore, in the calculation, it is necessary to first determine the proportion of reflected and transmitted sound energy, and then subtract these two proportions from the total incident sound energy (usually regarded as a baseline value of 100% or 1) to finally obtain the proportion of sound energy dissipated by the material, thereby quantifying the material's ability to absorb and dissipate incident sound energy. The calculation result is usually presented as a dimensionless value of 0-1 (or 0%-100%), which intuitively reflects the "absorption and dissipation efficiency" of the sound insulation material for incident sound energy. The closer the value is to 1 (or 100%), the stronger the material's ability to dissipate sound energy. For example, a certain sound insulation cotton has a calculated noise reduction value of 0.85 (i.e., 85%) for 500Hz sound waves. This means that 85% of the incident sound energy is absorbed and consumed by the material, and only 15% of the sound energy propagates through reflection or transmission. The noise reduction effect is mainly "absorption," making it suitable for scenarios where echo reduction is needed (such as bedrooms and conference rooms). The closer the value is to 0 (or 0%), the more it indicates that the material consumes almost no sound energy, and most of the sound energy is either reflected or penetrates the material. For example, a certain thin metal plate has a calculated noise reduction value of 0.12 (i.e., 12%), indicating that it mainly blocks sound waves through reflection rather than absorption. This may be more suitable for scenarios where echo control requirements are low but hard blocking is necessary (such as industrial equipment enclosures).

[0048] S3. Using the microstructure data, calculate the pore distribution entropy and connectivity index of the candidate sound insulation material, and analyze the sound wave propagation blocking efficiency of the candidate sound insulation material based on the pore distribution entropy and connectivity index.

[0049] This invention utilizes the microstructure data to calculate the pore distribution entropy and connectivity index of candidate sound insulation materials, transforming the abstract microstructure of the material into quantifiable indicators. This better reflects the uniformity and connectivity of the pores. For example, when screening porous sound insulation panels for buildings, these two indices can be used to determine whether the pores are uniform (avoiding excessively large local pores that lead to sound insulation gaps) and whether the connectivity is reasonable (preventing excessive connectivity that allows sound waves to penetrate), directly relating to the actual impact of the microstructure on sound insulation.

[0050] The pore distribution entropy refers to a statistical index that reflects the uniformity of pore size distribution, and the connectivity index refers to an index that reflects the degree of interconnection between pores inside the sound insulation material.

[0051] As an embodiment of the present invention, the pore distribution entropy and connectivity index of the candidate sound insulation material are calculated using the microstructure data, including: Using the microstructure data, a binary image of the pore structure of the candidate sound insulation material is obtained. Using the binary image of the pore structure, multiple pore size parameters of the pore structure in the candidate sound insulation material are identified; Based on the multiple pore size parameters, the pore distribution entropy of the candidate sound insulation material is calculated; Using the binary image of the pore structure, the number of connected paths of the candidate sound insulation material is identified, and based on the number of connected paths, the connectivity index of the candidate sound insulation material is calculated.

[0052] In practice, the microstructure data (such as scanning electron microscope (SEM) images with a resolution of 5000×5000 pixels) can first be converted to grayscale. Then, Gaussian filtering (with a standard deviation of 1.5 pixels) is used to remove noise. Finally, the Otsu's algorithm (maximum inter-class variance) is used to set a threshold (e.g., grayscale value 128, higher values ​​represent matrix, lower values ​​represent pores) for binarization segmentation, resulting in a binary image with clear boundaries between pores (black) and matrix (white). Using particle analysis software (such as ImageJ), the pore regions in the binary image are marked, and a minimum pore area threshold is set (e.g., 5 μm², excluding noise points smaller than this value). The equivalent diameter (approximately the diameter of a circle), area, and perimeter of each pore are automatically identified. The equivalent diameter is divided into five consecutive intervals (e.g., 10-30 μm, 30-50 μm…130-150 μm), and the frequency of pores in each interval relative to the total number of pores is statistically analyzed. Substitute into the information entropy formula For example, if the frequencies of a certain material in five intervals are 0.2, 0.2, 0.2, 0.2, and 0.2, the calculated values ​​are... H≈2.32 indicates a uniform pore size distribution; if the frequency is concentrated in a certain interval (e.g., 0.8, 0.1, 0.05, 0.03, 0.02), then H≈0.92, indicating a concentrated distribution. Morphological dilation is performed on the binary image (iterated 3 times to eliminate small gaps between pores), and then the connected component labeling algorithm is used to identify interconnected pore clusters (connected paths). The ratio of the number of connected pore clusters Nc to the total number of pores Nt is calculated, i.e., the connectivity index CI=Nc / Nt. For example, if a material has 100 total pores and 40 connected clusters are identified, then CI=0.4; if there are 70 connected clusters, then CI=0.7, indicating stronger pore connectivity.

[0053] Furthermore, this embodiment of the invention analyzes the sound wave propagation blocking efficiency of the candidate sound insulation materials based on the pore distribution entropy and the connectivity index. This transforms the pore distribution entropy and connectivity index into a basis for judging the sound wave propagation blocking effect, thereby linking the material's microstructure with its actual sound insulation capability. For example, when screening materials for air conditioner outdoor unit sound insulation covers, if the pore distribution entropy is uniform (pore size matches the outdoor unit's noise frequency band) and the connectivity index is low (reducing sound wave penetration channels), it can be directly determined that its propagation blocking efficiency for low-frequency noise from the air conditioner meets the standard.

[0054] The sound wave propagation blocking efficiency refers to the quantitative index of the ability of a sound insulation material to prevent sound waves from penetrating from one side of the material to the other. The higher the efficiency, the more significant the energy attenuation of the sound wave when it passes through the material.

[0055] As an embodiment of the present invention, the sound wave propagation hindrance efficiency of the candidate sound insulation material is analyzed based on the pore distribution entropy and the connectivity index, including: The intensity of the scattering effect of sound waves in the porous medium of the candidate sound insulation material is analyzed using the pore distribution entropy, and the intensity of the scattering effect is used as the first hindrance factor of the candidate sound insulation material. Using the connectivity index, the tortuosity and reflectivity of the sound wave propagation path in the candidate sound insulation material are analyzed to obtain the second hindrance factor. The overall acoustic wave hindrance coefficient of the candidate sound insulation material is calculated using the first hindrance factor and the second hindrance factor. Based on the comprehensive acoustic wave hindrance coefficient, the acoustic wave propagation hindrance efficiency of the candidate sound insulation material is determined.

[0056] The scattering effect intensity refers to the degree to which the propagation direction of a sound wave is changed by reflection and refraction at the interfaces of pores of different sizes and distributions in the porous medium of the candidate sound insulation material. The tortuosity refers to the ratio of the actual propagation path length to the material thickness when the sound wave penetrates the interior of the candidate sound insulation material. The reflectivity refers to the proportion of the incident energy reflected by the interface between the pores and the matrix when the sound wave propagates inside the candidate sound insulation material. The comprehensive sound wave blocking coefficient is a parameter that quantifies the overall sound wave propagation blocking ability of the material, calculated by comprehensively considering the scattering effect intensity (first blocking factor), path tortuosity, and reflectivity (second blocking factor).

[0057] In practice, the pore distribution entropy (H) can be normalized (range 0-1, H=0 corresponds to an extremely concentrated distribution, H=1 corresponds to a completely uniform distribution), and the first hindrance factor can be calculated using a scattering effect model (scattering intensity is positively correlated with H). =0.8×H+0.2 (ensure) (Range between 0.2 and 1.0). For example, for building partition wall materials, H=0.7, the calculated value is... =0.76, indicating a moderately strong scattering effect (uniform pores significantly increase multidirectional scattering of sound waves). Based on the negative correlation between connectivity index (CI) and path tortuosity (lower CI indicates poorer pore connectivity and more tortuous paths), a second hindering factor model is constructed. =1.2×(1-CI) (restriction) ≤1.0). For example, the CI of automotive sound insulation cotton is 0.3, calculated as follows: =1.2×0.7=0.84, indicating high path tortuosity and strong reflectivity; if CI=0.8, then =0.24 indicates a relatively smooth path with weak obstruction. Efficiency level thresholds are set (C≥0.8 for "high-efficiency obstruction", 0.5≤C<0.8 for "medium-efficiency obstruction", and C<0.5 for "low-efficiency obstruction"), the specific values ​​to be set based on the actual application scenario and requirements.

[0058] Furthermore, as another embodiment of the present invention, the step of calculating the comprehensive acoustic wave retardation coefficient of the candidate sound insulation material using the first retardation factor and the second retardation factor includes: The first blocking factor and the second blocking factor are coupled to obtain a coupling factor; The coupling factor is subjected to multivariate nonlinear regression calculation to identify the acoustic hysteresis coefficient of the candidate sound insulation material using the regression calculation results of the coupling factor. The acoustic wave hindrance coefficient is corrected for acoustic wave propagation confidence to obtain the comprehensive acoustic wave hindrance coefficient.

[0059] In practice, a coupling formula including interaction terms can be used for calculation, where the coupling factor K = × + 0.3× × ×( - (Where 0.3 is the interaction coefficient, calibrated using 100 material samples), to reflect the synergistic effect of the two factors. For example, building partition materials. =0.7、 =0.8, calculated K=0.7×0.8 + 0.3×0.7×0.8×(0.7-0.8)=0.56 - 0.0168=0.5432, this value comprehensively reflects the combined effect of scattering and path hindrance. Based on 100 sets of material sample data with known acoustic hindrance performance (including coupling factor K and corresponding measured hindrance coefficients), the least squares method is used to fit a multivariate nonlinear regression model: C0= 1.2×K²+ 0.5×K + 0.05 (model coefficients are determined through sample training, goodness of fit R²≥0.9), and the coupling factor K is substituted into the model to obtain the initial acoustic hindrance coefficient. For example, when K=0.5432, C0=1.2×0.5432²+ 0.5×0.5432 + 0.05≈0.35 + 0.27 + 0.05=0.67. By testing the sound wave transmission loss of the material (using the impedance tube method, test frequency 100-3000Hz, repeated 3 times and averaged), the deviation between the measured hysteresis coefficient and the regression result is calculated. C0 is corrected based on a 95% confidence interval (allowable deviation ±5%): if the deviation ≤5%, then the comprehensive coefficient C = C0; if the deviation >5%, then C = C0 × (1 - absolute value of deviation). For example, if C0 = 0.67 and the measured deviation is 3% (≤5%), then the comprehensive sound wave hysteresis coefficient C = 0.67, which meets the calculation accuracy requirements for building sound insulation materials.

[0060] S4. Based on the chemical composition data, analyze the molecular chain flexibility and interfacial compatibility of the candidate sound insulation material, and use the molecular chain flexibility and interfacial compatibility to evaluate the acoustic performance stability of the candidate sound insulation material when it is multi-component composite. This invention, by analyzing the molecular chain flexibility and interfacial compatibility of candidate sound insulation materials based on the chemical composition data, can start from the essential properties of the material and rely on the chemical composition data to accurately analyze the molecular chain flexibility (such as the resistance to deformation determined by polymer composition type and plasticizer content) and interfacial compatibility (such as component polarity and functional group matching degree), avoiding the one-sidedness of judging solely by appearance or short-term performance. For example, in the screening of automotive sound insulation composite panels, by analyzing the composition ratio and functional group type of rubber base raw materials and fiber layers, it can be determined in advance whether the rubber molecular chains are flexible enough to withstand vehicle body vibration, and whether the two components are compatible to avoid delamination during long-term use, directly ensuring the compatibility and durability of the material at the molecular level.

[0061] The molecular chain flexibility refers to the ease with which the material molecular chain deforms or moves under the action of external forces (such as sound wave vibration and environmental stress). The interface compatibility refers to the property that describes the tightness of the bonding between different component interfaces and whether delamination / cracking is likely to occur. The better the compatibility, the stronger the bonding between components.

[0062] In practice, the chemical bond energy of the main chain, the steric hindrance of the side chain, and the functional group information of the components of the candidate sound insulation material can be extracted through chemical composition data. Then, the molecular chain motion activity can be calculated to characterize the flexibility, and the polarity matching degree and interaction force between the components can be analyzed to evaluate the interfacial compatibility.

[0063] Furthermore, by utilizing the flexibility of the molecular chains and the compatibility of the interfaces, this embodiment of the invention evaluates the acoustic performance stability of candidate sound insulation materials after multi-component composites. This allows for precise prediction of the long-term acoustic performance stability of candidate materials after multi-component composites at the molecular and interfacial bonding levels, avoiding sound insulation effect degradation due to structural or component compatibility issues after composite formation. For example, when screening a composite system of "porous sound-absorbing cotton + epoxy resin adhesive layer" for building sound insulation, if the sound-absorbing cotton's molecular chains lack flexibility (easily cracking due to temperature and humidity changes) or have poor interfacial compatibility with epoxy resin (easily delaminating after long-term use), these problems can be identified in advance, preventing a sudden drop in the sound absorption coefficient after composite formation.

[0064] The acoustic performance stability refers to the ability of sound insulation materials to maintain their original levels of core acoustic performance, such as sound absorption coefficient and damping efficiency, under long-term use or environmental changes (such as temperature and humidity fluctuations, continuous vibration, and aging).

[0065] As an embodiment of the present invention, the acoustic performance stability of the candidate sound insulation material in a multi-component composite is evaluated by utilizing the flexibility of the molecular chain and the interfacial compatibility, including: Based on the molecular chain flexibility, construct the acoustic performance-flexibility relationship curve of the candidate sound insulation material; Based on the acoustic performance-flexibility relationship curve, the contribution of molecular chain flexibility to the acoustic stability of the candidate sound insulation material is analyzed to obtain the first contribution value; Based on the interface compatibility, construct the acoustic performance-compatibility relationship curve of the candidate sound insulation material; Using the acoustic performance-compatibility relationship curve, the contribution of the interface compatibility to the acoustic stability of the candidate sound insulation material is analyzed to obtain a second contribution value; Based on the first contribution value and the second contribution value, the acoustic performance stability of the candidate sound insulation material after multi-component composite is determined.

[0066] The acoustic performance-flexibility relationship curve refers to a curve used to intuitively reflect the correlation between molecular chain flexibility and acoustic performance stability. The first contribution value refers to a numerical value used to quantify the degree of influence of molecular chain flexibility on the acoustic stability of candidate sound insulation materials. For example, a normalized value of 0.6 for a certain material's curve indicates that molecular chain flexibility has a moderate contribution to its acoustic stability. The acoustic performance-compatibility relationship curve refers to a curve used to intuitively reflect the correlation between interface compatibility and acoustic performance stability. For example, the positive correlation curve of "the higher the compatibility index, the lower the sound absorption coefficient attenuation rate" for multi-layer sound insulation panels in buildings. The second contribution value refers to a numerical value used to quantify the degree of influence of interface compatibility on the acoustic stability of candidate sound insulation materials. For example, a normalized value of 0.7 for a certain material's curve indicates that interface compatibility has a significant contribution to its acoustic stability.

[0067] In practical implementation, the quantitative index of molecular chain flexibility (such as activation energy of chain segment motion, unit kJ / mol) can be used as the abscissa, and the rate of change of acoustic performance of the material after temperature cycling test (-20℃ to 60℃, 10 cycles) (such as the attenuation rate of sound absorption coefficient) can be used as the ordinate. A relationship curve can be constructed using at least 5 sets of sample test data with different flexibility parameters (e.g., activation energy of 30 kJ / mol corresponds to an attenuation rate of 2%, and 80 kJ / mol corresponds to an attenuation rate of 15%), and polynomial fitting (order 3, fitting error ≤ 5%). For example, in the testing of automotive sound insulation composite film materials, a negative correlation curve can be obtained where "the higher the activation energy, the greater the attenuation rate of the sound absorption coefficient". The absolute value of the curve slope is calculated (the larger the slope, the more significant the influence of flexibility on stability), and normalized to the range of 0-1 (maximum slope corresponds to 1, minimum slope corresponds to 0), which is then used as the first contribution value. For example, the absolute value of the curve slope of the aforementioned automotive sound insulation material is 0.003 (for every 1 kJ / mol increase in activation energy, the attenuation rate increases by 0.003%), which is at a medium level among similar materials. After normalization, the first contribution value is 0.6, indicating that molecular chain flexibility makes a moderate contribution to acoustic stability. Using the comprehensive interfacial compatibility index (range 0-1, 1 being complete compatibility) as the x-axis and the acoustic performance attenuation rate of the material after a damp heat aging test (temperature 40℃, humidity 90%, 1000h) as the y-axis, a linear fitting (R²≥0.9) curve was constructed using test data from six different compatibility samples (e.g., an index of 0.3 corresponds to an attenuation rate of 10%, and 0.9 corresponds to an attenuation rate of 3%). For example, in the testing of multi-layer sound insulation panels for buildings, a positive correlation curve can be obtained where "the higher the compatibility index, the lower the attenuation rate." The absolute value of the curve slope is calculated (the larger the absolute value, the more significant the impact of compatibility on stability), and then normalized to the 0-1 range as the second contribution value. For example, the absolute value of the slope of the curve for the aforementioned building sound insulation board is 0.05 (for every 0.1 increase in the compatibility index, the attenuation rate decreases by 0.05%), which has a significant impact among similar materials. After normalization, the second contribution value is 0.7, indicating that interfacial compatibility contributes significantly to acoustic stability. A weighted summation method is used to calculate the comprehensive stability index (in multiphase composite systems, interfacial compatibility has a weight of 0.6, and molecular chain flexibility has a weight of 0.4), with thresholds set (≥0.7 is "stable", 0.5-0.7 is "basically stable", and <0.5 is "unstable"). For example, a certain composite sound insulation material has a first contribution value of 0.6 and a second contribution value of 0.7, resulting in a comprehensive index of 0.6 × 0.4 + 0.7 × 0.6 = 0.66, which is considered "basically stable" and suitable for indoor environments with minimal fluctuations.

[0068] S5. Using the sound wave dissipation performance, the sound wave propagation hindrance efficiency, and the acoustic performance stability, select target materials from the candidate sound insulation materials.

[0069] This invention utilizes the sound wave dissipation performance, the sound wave propagation hindrance efficiency, and the acoustic performance stability to screen target materials from candidate sound insulation materials. This comprehensive approach, based on three core indicators—sound wave dissipation performance, sound wave propagation hindrance efficiency, and acoustic performance stability—avoids the problem of insufficient material adaptability in practical applications caused by relying solely on a single performance (such as only looking at the sound absorption coefficient).

[0070] As an embodiment of the present invention, a target material is screened from the candidate sound insulation materials by utilizing the sound wave dissipation performance, the sound wave propagation hindrance efficiency, and the acoustic performance stability, including: Query the actual application scenarios of the candidate sound insulation materials and the corresponding application requirements of the actual application scenarios; Based on the actual application scenario and the application requirements, weights are assigned to the sound wave dissipation performance, sound wave propagation hindrance efficiency and acoustic performance stability of the candidate sound insulation materials to obtain screening indicators. Based on the screening criteria, sound insulation materials that meet the application requirements are selected from the candidate sound insulation materials to obtain the target material.

[0071] The screening index refers to a comprehensive evaluation system that quantifies three core performance parameters—"sound wave dissipation performance," "sound wave propagation obstruction efficiency," and "acoustic performance stability"—based on the actual application scenarios and corresponding application requirements of candidate sound insulation materials. This system includes weight allocation and qualification thresholds.

[0072] In practice, the actual application scenario of candidate materials (such as "sound insulation material - application scenario association database") can be accessed (including automotive, construction, and industrial scenario classifications) to match the application scenario of the materials. Then, corresponding indicator requirements are retrieved from the scenario requirement database. For example, the engine compartment requires "high-frequency (2000-8000Hz) sound dissipation rate ≥35%, acoustic performance attenuation rate under temperature fluctuations ≤8%", while bedroom partitions require "low-frequency (50-500Hz) damping efficiency ≥0.7, long-term stability index ≥0.6". The Analytic Hierarchy Process (AHP) is used to assign values ​​based on scenario priority. For example, in the automotive engine compartment scenario, high-frequency noise is dominant, so sound dissipation performance has a weight of 0.4, damping efficiency a weight of 0.3, and stability a weight of 0.3; in the bedroom partition scenario, low-frequency sound insulation is crucial, so damping efficiency has a weight of 0.5, dissipation performance a weight of 0.2, and stability a weight of 0.3, forming a screening indicator system that includes indicator items, weights, and qualification thresholds. The candidate materials are scored on three performance aspects separately (standardized from 0 to 1, e.g., a material with a dissipation rate of 38% corresponds to 0.95 points). A comprehensive score is calculated based on weights (e.g., in an automotive scenario, the score = 0.4 × dissipation score + 0.3 × retardation score + 0.3 × stability score). A comprehensive score ≥ 0.8 is set as the passing threshold. For example, if a candidate material for an engine compartment scores 0.85 and all individual indicators meet the standards (dissipation score 0.9, retardation score 0.8, stability score 0.8), it is selected as the target material.

[0073] like Figure 2 The diagram shown is a functional block diagram of the intelligent screening system for the raw materials of sound insulation materials based on the present invention.

[0074] The intelligent raw material screening system 200 based on sound insulation materials described in this invention can be installed in an electronic device. Depending on the functions implemented, the intelligent raw material screening system based on sound insulation materials may include a data acquisition module 201, an acoustic performance analysis module 202, a material structure analysis module 203, a chemical performance analysis module 204, and a material screening module 205. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0075] In this embodiment of the invention, the functions of each module / unit are as follows: The data acquisition module 201 is used to acquire acoustic property test data, microstructure data and chemical composition data of candidate sound insulation materials; The acoustic performance analysis module 202 is used to decompose the acoustic characteristic test data into frequency response curves to obtain the characteristic frequency band sound absorption coefficient, use the characteristic frequency band sound absorption coefficient to construct the acoustic impedance tensor of the candidate sound insulation material, and use the acoustic impedance tensor to analyze the sound wave dissipation performance of the candidate sound insulation material. The material structure analysis module 203 is used to calculate the pore distribution entropy and connectivity index of the candidate sound insulation material using the microstructure data, and to analyze the sound wave propagation hindrance efficiency of the candidate sound insulation material based on the pore distribution entropy and connectivity index. The chemical performance analysis module 204 is used to analyze the molecular chain flexibility and interfacial compatibility of the candidate sound insulation material based on the chemical composition data, and to evaluate the acoustic performance stability of the candidate sound insulation material when it is multi-component composite using the molecular chain flexibility and the interfacial compatibility. The material screening module 205 is used to screen target materials from the candidate sound insulation materials by utilizing the sound wave dissipation performance, the sound wave propagation hindrance efficiency and the acoustic performance stability.

[0076] In detail, the modules in the intelligent raw material screening system 200 based on sound insulation materials described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The method described herein is the same as the intelligent screening method for the raw materials of sound insulation materials, and can produce the same technical effect, so it will not be repeated here.

[0077] 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.

[0078] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not 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 intelligent screening of raw materials based on the composition of soundproofing materials, characterized by, The method comprises: collecting acoustic characteristic test data, microstructure data and chemical composition data of a candidate sound insulation material; decomposing the acoustic characteristic test data into frequency response curves to obtain characteristic frequency band sound absorption coefficients, using the characteristic frequency band sound absorption coefficients to construct an acoustic impedance tensor of the candidate sound insulation material, and using the acoustic impedance tensor to analyze sound wave dissipation performance of the candidate sound insulation material; using the microstructure data to calculate a pore distribution entropy and a connectivity index of the candidate sound insulation material, and based on the pore distribution entropy and the connectivity index, analyzing sound wave propagation blocking efficiency of the candidate sound insulation material; based on the chemical composition data, analyzing molecular chain flexibility and interface compatibility of the candidate sound insulation material, and using the molecular chain flexibility and the interface compatibility to evaluate acoustic performance stability of the candidate sound insulation material in multi-component compounding; using the sound wave dissipation performance, the sound wave propagation blocking efficiency and the acoustic performance stability to screen a target material from the candidate sound insulation material.

2. The soundproofing material-based composition raw material intelligent screening method according to claim 1, characterized in that, Based on the pore distribution entropy and the connectivity index, analyzing the sound wave propagation blocking efficiency of the candidate sound insulation material comprises: using the pore distribution entropy to analyze scattering effect intensity of sound waves in a porous medium of the candidate sound insulation material, and taking the scattering effect intensity as a first blocking factor of the candidate sound insulation material; using the connectivity index to analyze tortuosity and reflectivity of a sound wave propagation path of sound waves in the candidate sound insulation material to obtain a second blocking factor; using the first blocking factor and the second blocking factor to calculate a comprehensive sound wave blocking coefficient of the candidate sound insulation material; based on the comprehensive sound wave blocking coefficient, determining the sound wave propagation blocking efficiency of the candidate sound insulation material.

3. The soundproofing material-based composition raw material intelligent screening method according to claim 2, characterized in that, The using the first blocking factor and the second blocking factor to calculate the comprehensive sound wave blocking coefficient of the candidate sound insulation material comprises: factor coupling the first blocking factor and the second blocking factor to obtain a coupling factor; multivariate nonlinear regression calculation of the coupling factor to identify a sound wave blocking coefficient of the candidate sound insulation material by using a regression calculation result of the coupling factor; sound wave transmission confidence correction of the sound wave blocking coefficient to obtain a comprehensive sound wave blocking coefficient.

4. The soundproofing material-based composition raw material intelligent screening method according to claim 1, characterized in that, Using the acoustic impedance tensor to analyze the sound wave dissipation performance of the candidate sound insulation material comprises: based on the acoustic impedance tensor, identifying a sound wave frequency and a sound wave incidence angle of sound waves incident into the candidate sound insulation material; based on the sound wave frequency and the sound wave incidence angle, calculating a sound intensity transmission coefficient and a sound intensity reflection coefficient of the candidate sound insulation material; using the sound intensity transmission coefficient and the sound intensity reflection coefficient to calculate a sound energy dissipation rate of the candidate sound insulation material; according to the sound energy dissipation rate, analyzing the sound wave dissipation performance of the candidate sound insulation material.

5. The soundproofing material-based composition raw material intelligent screening method according to claim 1, characterized in that, Using the molecular chain flexibility and the interface compatibility to evaluate the acoustic performance stability of the candidate sound insulation material in multi-component compounding comprises: according to the molecular chain flexibility, constructing an acoustic performance-flexibility relationship curve of the candidate sound insulation material; according to the interface compatibility, constructing an acoustic performance-interface compatibility relationship curve of the candidate sound insulation material; using the acoustic performance-flexibility relationship curve and the acoustic performance-interface compatibility relationship curve to evaluate the acoustic performance stability of the candidate sound insulation material in multi-component compounding. analyzing, based on the acoustic performance-flexibility relationship curve, a contribution of the molecular chain flexibility to acoustic stability of the candidate sound insulation material, to obtain a first contribution value; constructing, according to the interface compatibility, an acoustic performance-compatibility relationship curve of the candidate sound insulation material; analyzing, by using the acoustic performance-compatibility relationship curve, a contribution of the interface compatibility to acoustic stability of the candidate sound insulation material, to obtain a second contribution value; determining, according to the first contribution value and the second contribution value, acoustic performance stability of the candidate sound insulation material after multi-component compounding.

6. The intelligent screening method for raw materials of sound insulation materials as described in claim 1, characterized in that, calculating, by using the microstructure data, a pore distribution entropy and a connectivity index of the candidate sound insulation material, including: obtaining, by using the microstructure data, a pore structure binary image of the candidate sound insulation material, identifying, by using the pore structure binary image, a plurality of pore diameter parameters of the pore structure in the candidate sound insulation material; calculating, based on the plurality of pore diameter parameters, the pore distribution entropy of the candidate sound insulation material; identifying, by using the pore structure binary image, a number of connected paths of the candidate sound insulation material, and calculating, based on the number of connected paths, the connectivity index of the candidate sound insulation material.

7. The soundproofing material-based composition raw material intelligent screening method according to claim 1, wherein performing frequency response curve decomposition on the acoustic characteristic test data to obtain a characteristic frequency band sound absorption coefficient, including: preprocessing the acoustic characteristic test data to obtain a complete frequency response curve of the candidate sound insulation material; querying a target application scenario corresponding to the candidate sound insulation material, and identifying a noise frequency spectrum corresponding to the target application scenario; performing corresponding curve interception on the complete frequency response curve based on the noise frequency spectrum to obtain a target frequency response curve; performing integral operation on the target frequency response curve to obtain a characteristic frequency band sound absorption coefficient.

8. The soundproofing material-based composition raw material intelligent screening method according to claim 1, wherein constructing, by using the characteristic frequency band sound absorption coefficient, an acoustic impedance tensor of the candidate sound insulation material, including: decomposing the characteristic frequency band sound absorption coefficient into low-frequency coefficients, medium-frequency coefficients and high-frequency coefficients to obtain third-order characteristic frequency bands; converting the third-order characteristic frequency bands into a three-dimensional matrix; constructing, by using the three-dimensional matrix, the acoustic impedance tensor of the candidate sound insulation material.

9. The soundproofing material-based composition raw material intelligent screening method according to claim 1, wherein, screening, from the candidate sound insulation material, a target material by using the sound wave dissipation performance, the sound wave propagation retardation efficiency and the acoustic performance stability, including: querying an actual application scenario of the candidate sound insulation material and an application demand corresponding to the actual application scenario; based on the actual application scenario and the application demand, performing weight assignment on the sound wave dissipation performance, the sound wave propagation retardation efficiency and the acoustic performance stability of the candidate sound insulation material to obtain a screening index; screening, from the candidate sound insulation material, a sound insulation material meeting the application demand according to the screening index to obtain the target material.

10. A soundproofing material-based composition raw material intelligent screening system, characterized in that, The system includes: a data acquisition module configured to acquire acoustic characteristic test data, microstructure data and chemical composition data of a candidate sound insulation material; The acoustic performance analysis module is configured to perform frequency response curve decomposition on the acoustic characteristic test data to obtain characteristic frequency band sound absorption coefficients, construct an acoustic impedance tensor of the candidate sound insulation material by using the characteristic frequency band sound absorption coefficients, and analyze acoustic wave dissipation performance of the candidate sound insulation material by using the acoustic impedance tensor. The material structure analysis module is configured to calculate a pore distribution entropy and a connectivity index of the candidate sound insulation material by using the microstructure data, and analyze acoustic wave propagation blocking efficiency of the candidate sound insulation material based on the pore distribution entropy and the connectivity index. The chemical performance analysis module is configured to analyze molecular chain flexibility and interface compatibility of the candidate sound insulation material based on the chemical composition data, and evaluate acoustic performance stability of the candidate sound insulation material for multi-component compounding by using the molecular chain flexibility and the interface compatibility. The material screening module is configured to screen a target material from the candidate sound insulation material by using the acoustic wave dissipation performance, the acoustic wave propagation blocking efficiency, and the acoustic performance stability.

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