A coordinated control method and system for noise reduction and fire prevention in a substation

Abstract

The present invention discloses a coordinated control method and system for noise reduction and fire prevention in a substation. The method determines the degree of acoustic impedance mutation of an original composite panel based on the frequency and amplitude distribution of the substation's target low-frequency noise. When the degree of acoustic impedance mutation exceeds a mutation threshold, the first physical parameter of the original composite panel is optimized. The heat flux density of the optimized first composite panel is obtained. If the heat flux density exceeds the fire prevention threshold, the second physical parameter of the first composite panel is optimized. The noise penetration index of the target low-frequency noise in the optimized second composite panel is obtained. If the noise penetration index does not exceed the penetration threshold, the third physical parameter of the second composite panel is optimized. The final physical parameter corresponding to the acoustic-thermal performance balance point is determined based on the optimized acoustic impedance distribution and thermal conductivity distribution of the third composite panel to adjust the structure of the third composite panel. The present invention can achieve coordinated optimization of sound absorption frequency band characteristics and fire prevention performance, achieving ideal noise reduction and fire prevention simultaneously.

Description

Technical Field

[0001] The present invention relates to the technical field of substations, and in particular to a coordinated control method and system for noise reduction and fire prevention in a substation. Background Art

[0002] The coordinated control of noise reduction and fire prevention within the station is a key measure for environmental protection and safe operation of power substations. Its importance lies in its ability to effectively reduce the impact of the main transformer's low-frequency noise on the surrounding environment, while ensuring the high-temperature protection capability of the equipment, providing guarantees for the long-term stable operation of the substation.

[0003] Current noise reduction and fire prevention methods for substations typically utilize single-functional materials or simple composite materials, such as single sound-absorbing panels or fire-retardant coatings. These methods are significantly inadequate in balancing the optimization of the sound absorption band with improved fire protection performance. This is particularly true when the composite material's structure changes. The limitations of these methods primarily lie in the inadequate control of the composite material's interface properties, making it difficult to precisely regulate the combined impact of these interface properties on acoustic and thermal properties. This leads to an imbalance between noise reduction and fire protection, making it difficult to achieve both ideal states simultaneously. Therefore, the core challenge currently faced lies in precisely regulating the composite layer's interface properties to achieve synergistic optimization of the sound absorption band and fire protection performance, thereby meeting the substation's dual needs for low-frequency noise control and high-temperature protection. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a method and system for coordinated control of noise reduction and fire prevention in a substation. By precisely controlling the comprehensive impact of the interface characteristics of the composite panels on the acoustic and thermal properties, it is possible to achieve coordinated optimization of the sound absorption frequency band characteristics and the fire prevention performance, so that the noise reduction effect and the fire prevention effect can reach an ideal state at the same time, thereby meeting the dual needs of the substation for low-frequency noise control and high-temperature protection.

[0005] To achieve the above objectives, an embodiment of the present invention provides a method for coordinated control of noise reduction and fire prevention in a substation, comprising:

[0006] Obtaining raw data of the substation, and obtaining the frequency and amplitude distribution of target low-frequency noise based on the raw data, wherein the raw data includes equipment operation load data and sound pressure level time series data;

[0007] Determining the degree of acoustic impedance mutation between two adjacent layers of media of an original composite plate of a substation according to the frequency and amplitude distribution, wherein the original composite plate is composed of a floating bead plate layer and an aluminum silicate fiber cotton layer;

[0008] When the degree of the acoustic impedance mutation exceeds a preset mutation threshold, optimizing the first physical parameter of the original composite plate to obtain a first composite plate;

[0009] Obtaining a heat flux density of the first composite panel, and when the heat flux density exceeds a preset fire protection threshold, optimizing a second physical parameter of the first composite panel to obtain a second composite panel;

[0010] Obtaining a noise penetration index of the target low-frequency noise in the second composite panel, and when the noise penetration index does not exceed a preset penetration threshold, optimizing a third physical parameter of the second composite panel to obtain a third composite panel;

[0011] Acquiring the acoustic impedance distribution and thermal conductivity distribution of the third composite plate, and determining final physical parameters of the third composite plate corresponding to an acoustic-thermal performance balance point based on the acoustic impedance distribution and the thermal conductivity distribution, so as to adjust the structure of the third composite plate.

[0012] To achieve the above objectives, an embodiment of the present invention further provides a coordinated control system for noise reduction and fire prevention in a substation, comprising:

[0013] A noise frequency and amplitude distribution acquisition module is used to acquire raw data from the substation and obtain the frequency and amplitude distribution of the target low-frequency noise based on the raw data, wherein the raw data includes equipment operation load data and sound pressure level time series data;

[0014] an acoustic impedance mutation degree determination module, configured to determine the acoustic impedance mutation degree between two adjacent layers of a substation's original composite board based on the frequency and amplitude distribution, the original composite board comprising a bead board layer and an aluminum silicate fiber cotton layer;

[0015] A first physical parameter optimization module is configured to optimize the first physical parameter of the original composite plate to obtain a first composite plate when the degree of the acoustic impedance mutation exceeds a preset mutation threshold;

[0016] a second physical parameter optimization module, configured to obtain a heat flux density of the first composite panel, and when the heat flux density exceeds a preset fire protection threshold, optimize a second physical parameter of the first composite panel to obtain a second composite panel;

[0017] a third physical parameter optimization module, configured to obtain a noise penetration index of the target low-frequency noise in the second composite panel, and, when the noise penetration index does not exceed a preset penetration threshold, optimize a third physical parameter of the second composite panel to obtain a third composite panel;

[0018] an acoustic-thermal performance balance control module, configured to obtain an acoustic impedance distribution and a thermal conductivity distribution of the third composite plate, and determine final physical parameters of the third composite plate corresponding to an acoustic-thermal performance balance point based on the acoustic impedance distribution and the thermal conductivity distribution, so as to adjust the structure of the third composite plate.

[0019] Compared with the existing technology, the embodiments of the present invention provide a coordinated control method and system for noise reduction and fire prevention in a substation. By precisely controlling the comprehensive influence of the interface characteristics of the composite panels on the acoustic and thermal properties, it is possible to achieve coordinated optimization of the sound absorption frequency band characteristics and the fire prevention performance, so that the noise reduction effect and the fire prevention effect can reach an ideal state at the same time, thereby meeting the dual needs of the substation for low-frequency noise control and high-temperature protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of a method for coordinated control of noise reduction and fire prevention in a substation provided by one embodiment of the present invention;

[0021] Figure 2 This is a structural block diagram of a coordinated control system for noise reduction and fire prevention in a substation provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this technical field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] It should be noted that the limitations of existing noise reduction and fire prevention control methods for substations are mainly reflected in the insufficient control of the interface characteristics of composite materials. Traditional designs often ignore key factors such as the sudden change in acoustic impedance at the interface between the bead board and the aluminum silicate fiber cotton composite layer, the ratio of sound energy reflection and transmission, and the reconstruction of the heat conduction path. These factors will directly affect the breadth of the sound absorption band and the stability of the fire prevention performance, but they have not been fully utilized due to the lack of a systematic collaborative control strategy. Among them, the degree of mutation of the interface acoustic impedance determines the ratio of sound energy reflection and transmission at the interface, which directly affects the effectiveness of the sound absorption band. The reconstruction of the heat conduction path changes the fire prevention performance of the composite layer and increases the design complexity. It can be seen that these technical factors are coupled with each other. If they cannot be effectively coordinated and controlled, it will lead to an imbalance between the noise reduction effect and fire prevention capability of the substation, making it difficult to meet the dual needs of the substation for low-frequency noise control and high-temperature protection. Therefore, how to optimize the heat conduction path of the interface acoustic impedance by adjusting the structure of the composite layer of the floating bead board and the aluminum silicate fiber cotton, so as to achieve a synergistic improvement in the sound absorption frequency band characteristics and fire protection performance, has become a key issue in the coordinated control of noise reduction and fire prevention in substations.

[0024] In order to solve the above problems, the embodiment of the present invention provides a coordinated control method for noise reduction and fire prevention in a substation. Figure 1FIG. 1 is a flow chart of a method for coordinated control of noise reduction and fire prevention in a substation provided by an embodiment of the present invention. The method includes steps S11 to S16:

[0025] Step S11: obtaining raw data of the substation, and obtaining the frequency and amplitude distribution of the target low-frequency noise based on the raw data, wherein the raw data includes equipment operation load data and sound pressure level time series data.

[0026] It should be noted that a sensor array can be used to obtain raw data from the substation in a fixed time series. The raw data includes the equipment operating load data of the substation and the sound pressure level time series data when the substation is in operation; among them, the equipment operating load data represents the working status of the equipment in the substation, and the sound pressure level time series data represents the changes in noise intensity generated by the equipment in the substation at different times during operation.

[0027] For example, in practical applications, the sensor array may use 8 omnidirectional microphones, which are evenly arranged around relevant equipment in the substation at a spacing of 4 meters. The sampling frequency may be set to 4096 Hz, and the sampling time may be set to 30 minutes.

[0028] It should be noted that the low-frequency noise generated during the operation of the substation has complex spectral characteristics. The embodiment of the present invention can establish a complete noise feature recognition process by collecting and analyzing the original data of the substation.

[0029] In one optional embodiment, obtaining the frequency and amplitude distribution of the target low-frequency noise according to the original data specifically includes:

[0030] De-noising and standardizing the raw data to obtain standardized equipment operation load data and standardized sound pressure level time series data;

[0031] Segmenting the standardized sound pressure level time series data according to the standardized equipment operation load data, and performing band-pass filtering on the segmented sound pressure level time series data according to a preset target frequency band to obtain a target low-frequency band signal;

[0032] performing multi-scale wavelet decomposition and Hilbert transform on the target low-frequency band signal in sequence to obtain a time-frequency distribution of the target low-frequency band signal;

[0033] Frequency components whose amplitudes exceed a preset noise reference value are extracted from the time-frequency distribution, and spectrum analysis is performed using fast Fourier transform to obtain the frequency and amplitude distribution of the target low-frequency band noise.

[0034] Specifically, when obtaining the frequency and amplitude distribution of the target low-frequency band noise based on the original data of the substation, the original data can be denoised first to remove outliers, and the denoised original data can be normalized (for example, normalized using the maximum and minimum value normalization method), and corresponding standardized equipment operation load data and standardized sound pressure level time series data are obtained; then, the standardized sound pressure level time series data is segmented according to the standardized equipment operation load data, and the segmented sound pressure level time series data is band-pass filtered according to the preset target frequency band to extract the target low-frequency band signal; thereafter, the extracted target low-frequency band signal can be subjected to multi-scale wavelet decomposition using a wavelet decomposition method, and detail coefficients at multiple scales are obtained (these detail coefficients represent signal components of different frequencies), and the detail coefficients at multiple scales are subjected to Hilbert transform. For the detail coefficient sequence x(t) at any scale, its Hilbert transform H[x(t)] can be defined as: , Represents the convolution operation. Furthermore, a complex analytical signal z(t) can be constructed based on x(t) and its Hilbert transform H[x(t)], and we have: , j represents the imaginary unit, and the instantaneous frequency ω(t) and instantaneous amplitude A(t) of the target low-frequency band signal are calculated based on the analytical signal z(t), and: , , represents the instantaneous phase, and: , according to the instantaneous frequency ω(t) and instantaneous amplitude A(t), the time-frequency distribution of the target low-frequency band signal can be obtained; finally, the frequency components whose amplitudes exceed the preset noise baseline value are extracted from the time-frequency distribution, and the fast Fourier transform (FFT) is used to perform spectral analysis on the extracted frequency components to obtain the frequency and amplitude distribution of the target low-frequency band noise accordingly.

[0035] It should be noted that in the preprocessing stage of the raw data, the raw data can be denoised to eliminate abnormal values ​​that exceed the normal range. For example, when the collected instantaneous value of the sound pressure level exceeds 120dB or is lower than 30dB, it is judged as an abnormal value and eliminated. For the preprocessed raw data, the maximum and minimum value normalization method can be used for normalization, and the numerical range of the preprocessed raw data can be uniformly mapped to between 0 and 1 for subsequent analysis and processing.

[0036] It should be noted that when the standardized sound pressure level time series data is segmented according to the standardized equipment operation load data, different load rate intervals can be defined. For example, the interval with a load rate greater than 80% is divided into a heavy load segment, the interval with a load rate between 50% and 80% is divided into a medium load segment, and the interval with a load rate less than 50% is divided into a light load segment; for the sound pressure level data at each time point in the sound pressure level time series data, the corresponding equipment operation load data (that is, the load rate at that time point) is found. Once the load rates of all time points are determined, the sound pressure level time series data can be segmented according to the load rate intervals defined above. For example, if the load rate at a certain time point belongs to the heavy load segment, the sound pressure level data at that time point is classified as the data of the heavy load segment. Similarly, the data of other load rate intervals are classified.

[0037] For example, in the frequency analysis stage, the segmented sound pressure level time series data can first be band-pass filtered from 20Hz to 200Hz to extract the target low-frequency band signal, and then the extracted target low-frequency band signal can be subjected to a 5-layer wavelet decomposition using a wavelet decomposition method to obtain detail coefficients of different frequency bands. Subsequently, the instantaneous frequency and instantaneous amplitude are calculated by Hilbert transform to construct a time-frequency distribution diagram, which can intuitively display the time-varying characteristics of the low-frequency band noise frequency. Afterwards, when the amplitude of a certain frequency component exceeds the ambient background noise baseline value by more than 5dB, it can be extracted as a characteristic frequency component, and the extracted characteristic frequency component can be subjected to spectrum analysis using fast Fourier transform to obtain the frequency-amplitude distribution.

[0038] It should be noted that in actual applications, it can be observed that when the transformers in the substation are operating under heavy load, they mainly generate low-frequency noise of 100Hz and 150Hz, which corresponds to the vibration frequency of the transformer core. The main frequency components can be identified through the peak detection algorithm, and feature extraction can be performed in combination with the environmental background noise. The energy proportion and signal-to-noise ratio of each frequency point can also be calculated. For example, in a certain measurement, the energy proportion of the 100Hz frequency component reached 35%, and the signal-to-noise ratio was 15dB. The energy proportion of the 150Hz frequency component was 25%, and the signal-to-noise ratio was 12dB. These characteristic values ​​constitute the low-frequency noise feature description vector under this working condition.

[0039] Step S12: determining the degree of acoustic impedance mutation between two adjacent layers of the original composite board of the substation according to the frequency and amplitude distribution, wherein the original composite board is composed of a floating bead board layer and an aluminum silicate fiber cotton layer.

[0040] It should be noted that the original composite panels of the substation include a floating bead board layer and an aluminum silicate fiber cotton layer. The floating bead board layer and the aluminum silicate fiber cotton layer are stacked to form a composite layer structure, and the composite layer structure has unique acoustic and thermal properties in terms of noise reduction and fire prevention.

[0041] In one optional embodiment, determining the degree of acoustic impedance mutation between two adjacent layers of media of an original composite plate of a substation according to the frequency and amplitude distribution specifically includes:

[0042] According to the frequency and amplitude distribution, combined with the original physical parameters of the original composite plate of the substation, constructing the three-dimensional structure of the original composite plate;

[0043] Calculating a relative change rate of the acoustic impedance between the floating bead layer and the aluminum silicate fiber cotton layer according to the interface acoustic impedance between the floating bead layer and the aluminum silicate fiber cotton layer in the three-dimensional structure;

[0044] According to the relative change rate, the degree of acoustic impedance mutation between the floating bead plate layer and the aluminum silicate fiber cotton layer is determined.

[0045] Specifically, in combination with the above embodiment, when determining the degree of acoustic impedance mutation between two adjacent layers of the original composite board according to the frequency and amplitude distribution of the target low-frequency noise, the frequency and amplitude distribution data of the target low-frequency noise can be combined with the original physical parameters of the original composite board of the substation (including the density, elastic modulus and Poisson's ratio of the bead board layer, and the density, elastic modulus and Poisson's ratio of the aluminum silicate fiber cotton layer), and the three-dimensional structure of the original composite board can be constructed using the tetrahedral mesh partitioning method, and the pore size of the bead board layer and the fiber arrangement of the aluminum silicate fiber cotton layer can be set. Column density; then, based on the interface acoustic impedance of the floating bead layer and the interface acoustic impedance of the aluminum silicate fiber cotton layer in the three-dimensional structure, the relative change rate of the acoustic impedance between the floating bead layer and the aluminum silicate fiber cotton layer is calculated, where the acoustic impedance Z can be defined as: Z=ρ×c, ρ represents the medium density, c represents the propagation speed of sound waves in the medium (i.e., the speed of sound), reflecting the ability of the medium to hinder the propagation of sound waves. Assuming that the acoustic impedance of the floating bead layer is Z1 and the acoustic impedance of the aluminum silicate fiber cotton layer is Z2, then the relative change rate of the acoustic impedance between the two layers of media is: ; Afterwards, the degree of acoustic impedance mutation between the floating bead board layer and the aluminum silicate fiber cotton layer can be determined based on the calculated relative change rate of acoustic impedance. It can be understood that the greater the relative change rate of acoustic impedance, the greater the degree of acoustic impedance mutation between the floating bead board layer and the aluminum silicate fiber cotton layer.

[0046] For example, the density of the floating bead layer can be set at 300 to 500 kg / m 3 The elastic modulus of the floating bead layer can be set between 0.5 and 2 GPa, the Poisson's ratio of the floating bead layer can be set to 0.25; the density of the aluminum silicate fiber cotton layer can be set to 80 to 150 kg / m 3The elastic modulus of the aluminum silicate fiber cotton layer can be set between 0.1 and 0.3 GPa, and the Poisson's ratio of the aluminum silicate fiber cotton layer can be set to 0.3; these original physical parameters jointly determine the acoustic and thermal properties of the original composite panel.

[0047] It should be noted that when the tetrahedral meshing method is used to construct the three-dimensional structure of the original composite plate, the choice of mesh size has an important impact on the subsequent calculation accuracy. For example, for the floating bead plate layer, when its pore size distribution is in the range of 0.1 to 0.5 mm, the mesh size can be set to 1 / 6 of the minimum pore size, which is approximately 0.017 mm. For the aluminum silicate fiber cotton layer, when its fiber arrangement density is between 300 and 800 fibers per square centimeter, the mesh size can be set to 1 / 4 of the fiber diameter, which is approximately 0.008 mm. The mesh size is determined based on the characteristic dimensions of the floating bead plate layer and the aluminum silicate fiber cotton layer. Such meshing ensures the calculation accuracy and avoids excessive calculation.

[0048] It should be noted that when sound waves propagate through the original composite plate, their angle of incidence significantly affects the acoustic energy transfer. For example, when the angle of incidence is between 0° and 30°, the sound waves propagate primarily in the normal direction. At this time, the acoustic impedance of the bead plate layer is approximately 0.8 MPa·s / m, and the acoustic impedance of the aluminum silicate fiber layer is approximately 0.15 MPa·s / m. When the angle of incidence increases to between 30° and 60°, the propagation path of the sound wave lengthens, the number of interface reflections increases, and the equivalent acoustic impedances of the two media increase to 1.2 MPa·s / m and 0.25 MPa·s / m, respectively. In interfacial acoustic impedance analysis, the relative rate of change of acoustic impedance between adjacent media reflects the degree of obstruction to acoustic energy transfer. Taking a 45° angle of incidence as an example, when the relative rate of change of acoustic impedance at the interface exceeds 0.5, the interfacial stress distribution exhibits significant discontinuity, and the stress concentration factor reaches 2.5. In this case, the proportion of acoustic energy reflected at the interface increases significantly.

[0049] For example, the propagation of sound energy in the original composite plate follows the principle of conservation of energy. The sound energy distribution characteristics obtained by calculation show that at a frequency of 100 Hz, when the incident sound pressure level is 80 dB, the reflection coefficient of the first layer interface is approximately 0.4, and the transmission coefficient is 0.6, and the reflection coefficient of the second layer interface is approximately 0.3, and the transmission coefficient is 0.7; after considering the nonlinear characteristics of the dielectric material, when the frequency increases to 200 Hz, the degree of mutation of the interface acoustic impedance increases, resulting in the reflection coefficient of the first layer interface increasing to 0.5 and the transmission coefficient decreasing to 0.5, and the reflection coefficient of the second layer interface increasing to 0.4 and the transmission coefficient decreasing to 0.6. This frequency dependence shows that the composite layer structure of the original composite plate has a selective attenuation effect on noise of different frequencies.

[0050] Step S13: When the degree of the acoustic impedance mutation exceeds a preset mutation threshold, optimizing the first physical parameter of the original composite plate to obtain a first composite plate.

[0051] It should be noted that after obtaining the degree of acoustic impedance mutation between the floating bead board layer and the aluminum silicate fiber cotton layer (i.e., the relative rate of change of acoustic impedance between the two layers of media), the degree of acoustic impedance mutation between the floating bead board layer and the aluminum silicate fiber cotton layer can be compared with a preset mutation threshold. If it is determined that the degree of acoustic impedance mutation exceeds the preset mutation threshold, the first physical parameter of the original composite board can be optimized, and the composite board with the optimized first physical parameter can be obtained accordingly, and used as the first composite board.

[0052] In one optional embodiment, the first physical parameter includes the porosity of the floating bead layer and the density of the aluminum silicate fiber cotton layer;

[0053] Then, optimizing the first physical parameter of the original composite plate to obtain the first composite plate specifically includes:

[0054] Obtaining a first parameter set according to the preset adjustment range and adjustment amplitude of the porosity of the floating bead layer and the adjustment range and adjustment amplitude of the density of the aluminum silicate fiber cotton layer;

[0055] Selecting from the first parameter set several parameter combinations that satisfy the lower limit of the compressive strength of the floating bead board layer and the upper limit of the elastic modulus of the aluminum silicate fiber cotton layer to obtain a second parameter set;

[0056] Selecting from the second parameter set several groups of parameter combinations that satisfy the total thickness range and interlayer bonding area ratio of the original composite plate to obtain a third parameter set;

[0057] For each parameter combination in the third parameter set, calculating the ratio of the corresponding adjacent interface acoustic impedances as the acoustic impedance matching degree;

[0058] Iteratively optimizing the parameter combination in the third parameter set according to the acoustic impedance matching to obtain a final parameter combination, wherein during the iterative optimization process, the adjustment range of the porosity of the floating bead plate layer and the adjustment range of the density of the aluminum silicate fiber cotton layer decrease as the number of iterations increases;

[0059] The porosity of the floating bead board layer and the density of the aluminum silicate fiber cotton layer are optimized according to the final parameter combination to obtain a first composite board.

[0060] Specifically, in combination with the above embodiment, the first physical parameter of the original composite board includes the porosity of the floating bead board layer and the density of the aluminum silicate fiber cotton layer. Accordingly, when actually optimizing the first physical parameter of the original composite board, the adjustment range of the porosity of the floating bead board layer (for example, floating 10% above and below the original value) and the adjustment amplitude (i.e., the adjustment step length, which is the initial value, for example, the initial adjustment step length is 0.5%), the adjustment range of the density of the aluminum silicate fiber cotton layer (for example, floating 20% ​​above and below the original value) and the adjustment amplitude (i.e., the adjustment step length, which is the initial value, for example, the initial adjustment step length is 2kg / m 3), and the first parameter set is obtained accordingly. The first parameter set includes multiple parameter combinations, and each parameter combination consists of the porosity of a floating bead layer (a porosity value within the porosity adjustment range) and the density of an aluminum silicate fiber cotton layer (a density value within the density adjustment range); for the first parameter set, several parameter combinations that meet the following physical constraints are screened out: the compressive strength of the floating bead layer needs to be maintained above the lower limit of the compressive strength, and the elastic modulus of the aluminum silicate fiber cotton layer needs to be maintained below the upper limit of the elastic modulus, and the second parameter set is obtained accordingly, wherein, for each parameter combination in the first parameter set, the actual compressive strength of the corresponding floating bead layer and the actual elastic modulus of the aluminum silicate fiber cotton layer are determined. Modulus, and judge whether the actual compressive strength of the floating bead board layer and the actual elastic modulus of the aluminum silicate fiber cotton layer corresponding to each set of parameter combinations meet the above physical constraints. If so, the corresponding parameter combination is retained, otherwise, the corresponding parameter combination is excluded, so as to form a second parameter set based on all the retained parameter combinations; for the second parameter set, several groups of parameter combinations that meet the following geometric constraints are screened out: the total thickness of the original composite board should be within a certain range (for example, between 50 and 80 mm), and the ratio of the interlayer bonding area should exceed a certain ratio value (for example, more than 85%), and the third parameter set is obtained accordingly, in which the total thickness of the original composite board is the thickness of the floating bead board layer and the thickness of the aluminum silicate fiber cotton layer. The sum of the interlayer bonding area ratio is the ratio of the contact area between the floating bead board layer and the aluminum silicate fiber cotton layer to the total area. Accordingly, for each parameter combination in the second parameter set, the actual total thickness of the corresponding composite board and the actual interlayer bonding area ratio are calculated, and it is judged whether the actual total thickness and the actual interlayer bonding area ratio corresponding to each parameter combination meet the above-mentioned geometric constraints. If so, the corresponding parameter combination is retained, otherwise, the corresponding parameter combination is excluded, and the third parameter set is formed according to all the retained parameter combinations; for each parameter combination in the third parameter set, according to the density ρ1 and the sound velocity c1 of the floating bead board layer, the acoustic impedance of the corresponding floating bead board layer is calculated as: Z1=ρ1×c1, and the same According to the density and sound velocity of the aluminum silicate fiber cotton layer, the acoustic impedance of the corresponding aluminum silicate fiber cotton layer is calculated as Z2. Then, according to the acoustic impedance Z1 of the floating bead plate layer and the acoustic impedance Z2 of the aluminum silicate fiber cotton layer corresponding to each set of parameter combinations, the ratio of the corresponding adjacent interface acoustic impedances can be calculated as: Ratio = Z2 / Z1, and the calculated ratio is used as the acoustic impedance matching degree. Afterwards, the parameter combinations in the third parameter set are iteratively optimized. Each round of iteration can calculate the parameter update direction based on the acoustic impedance matching degree, and the adjustment range of the porosity of the floating bead plate layer and the adjustment range of the density of the aluminum silicate fiber cotton layer decreases with the increase of the number of iterations. For example, the initial adjustment range of the porosity of the floating bead plate layer is 0.5%, and the adjustment range increases from 0.Starting from 5%, the parameter value decreases with the number of iterations. After the iteration process is completed, a final parameter combination is obtained. This final parameter combination includes the porosity of the floating bead layer and the density of the aluminum silicate fiber cotton layer determined through iterative optimization. Finally, based on the final parameter combination obtained, the current porosity of the floating bead layer and the current density of the aluminum silicate fiber cotton layer are adjusted respectively to obtain the first composite board.

[0061] It should be noted that in the actual iterative optimization process, for any set of parameter combinations in the third parameter set, the acoustic impedance matching under this set of parameter combinations can be calculated first, and then the influence of the changes in each parameter (i.e., the porosity of the floating bead layer and the density of the aluminum silicate fiber cotton layer) on the acoustic impedance matching can be estimated. Assuming that the parameter vector composed of each parameter is X, the gradient of the acoustic impedance matching function f(X) is This means the direction and rate at which the acoustic impedance matching changes with the changes in various parameters. Therefore, the parameters can be updated based on the gradient information, and we have: , X old represents the parameter vector before update, η represents the learning rate, which is used to control the step size of each update, X new represents the updated parameter vector; further, the iteration process ends when any of the following conditions is met:

[0062] (1) Reaching the maximum number of iterations: A maximum number of iterations is set in advance. When the actual number of iterations reaches the maximum number of iterations, the iteration is stopped;

[0063] (2) When the change in the acoustic impedance matching degree in several consecutive iterations is less than a certain threshold, it is considered to have converged and the iteration is stopped. For example, if the change in the acoustic impedance matching degree in five consecutive iterations is less than 0.001, it is considered to have reached the convergence standard and the iteration is stopped.

[0064] (3) When the change in the parameters is less than a certain threshold, the iteration is stopped; for example, if the changes in the porosity of the floating bead layer and the density of the aluminum silicate fiber cotton layer are both less than 0.01%, the iterative optimization process is considered to be completed and the iteration is stopped.

[0065] For example, when the degree of acoustic impedance mutation exceeds the preset mutation threshold, the acoustic energy reflection at the surface interface is strong, and the structure of the original composite board needs to be optimized and adjusted; taking the low-frequency noise control of a substation as an example, the porosity of the floating bead board layer in the original composite board is 45%, and the density of the aluminum silicate fiber cotton layer is 120kg / m 3, the acoustic impedance mutation degree is 0.75, and the preset mutation threshold is 0.6. At this time, the acoustic impedance mutation degree exceeds the preset mutation threshold, and the porosity of the floating bead layer and the density of the aluminum silicate fiber cotton layer need to be adjusted; during the parameter adjustment process, the adjustment range of the porosity of the floating bead layer is limited to between 40.5% and 49.5%, and the adjustment step size is 0.5% each time; the adjustment range of the density of the aluminum silicate fiber cotton layer is between 96 and 144 kg / m 3 The adjustment step is 2kg / m 3 In terms of physical parameter constraints, the compressive strength of the floating bead layer must be maintained above 2.5MPa, and the elastic modulus of the aluminum silicate fiber cotton layer must not exceed 0.3GPa. These physical constraints ensure that the floating bead layer and the aluminum silicate fiber cotton layer still have sufficient mechanical strength after adjusting the porosity and density; in terms of geometric parameter constraints, the total thickness of the original composite board is controlled within the range of 50 to 80mm, and the interlayer bonding area ratio is maintained above 85%; when the porosity of the floating bead layer is adjusted to 47%, its compressive strength is reduced to 2.8MPa, which still meets the compressive strength limit requirement. At the same time, the density of the aluminum silicate fiber cotton layer is increased to 130kg / m 3 , the corresponding elastic modulus is 0.25 GPa, which still meets the elastic modulus limit requirements. Under this parameter combination, the acoustic impedance ratio of adjacent interfaces is reduced from the original 2.8 to 1.8. Sound wave propagation calculations show that at a frequency of 100 Hz, the sound energy reflection ratio of the optimized composite layer structure (i.e., the first composite plate) is reduced from the original 0.45 to 0.35, and the transmission ratio is increased from the original 0.55 to 0.65.

[0066] Furthermore, assuming that in the 10th iteration, the porosity of the floating bead layer is adjusted to 48%, and the density of the aluminum silicate fiber cotton layer is adjusted to 135 kg / m 3 At this time, the degree of acoustic impedance mutation is reduced to 0.55, which is lower than the preset mutation threshold. At the same time, the total thickness of the composite plate corresponding to this parameter combination is 75 mm, and the interlayer bonding area ratio is 88%, which meets all the geometric parameter constraints. For the determined physical parameter combination, the optimized composite layer structure maintains sufficient mechanical strength while improving the interface acoustic impedance matching by 35%. The average acoustic energy reflection ratio in the frequency band of 80 to 200 Hz is reduced to 0.32, and the transmission ratio is increased to 0.68, indicating that the optimized composite layer structure can more effectively reduce low-frequency noise.

[0067] Step S14: obtaining the heat flux density of the first composite panel, and when the heat flux density exceeds a preset fire protection threshold, optimizing the second physical parameter of the first composite panel to obtain a second composite panel.

[0068] It should be noted that after obtaining the first composite panel through parameter adjustment, the heat flux density distribution inside the first composite panel structure can be further obtained, and the obtained heat flux density can be compared with the preset fire protection threshold to determine whether the first composite panel meets the fire protection requirements, and output the fire protection performance evaluation result including the heat flux density distribution characteristics. If it is determined that the heat flux density exceeds the preset fire protection threshold, it means that the fire protection requirements are not met at this time, and it is necessary to optimize the second physical parameter of the first composite panel, and obtain the composite panel with the optimized second physical parameter accordingly, and use it as the second composite panel.

[0069] In one optional embodiment, obtaining the heat flux density of the first composite plate specifically includes:

[0070] Establishing a three-dimensional grid unit of the first composite board, and adding the thermal conductivity, density, and specific heat capacity of the floating bead board layer and the aluminum silicate fiber cotton layer to a grid unit attribute table;

[0071] The external heat source temperature and the ambient temperature are used as the first-type boundary conditions, and the convection heat transfer coefficient is used as the third-type boundary condition. The heat conduction equation is established in each grid cell.

[0072] The heat conduction equation is discretized and solved using the finite volume method to obtain the temperature field distribution;

[0073] According to the temperature field distribution, a temperature gradient is obtained using a central difference method;

[0074] According to the temperature gradient, a heat flow line tracing algorithm is used to identify a heat conduction path, and a heat flux density on the heat conduction path is obtained.

[0075] Specifically, in combination with the above embodiment, when obtaining the heat flux density of the first composite plate, the floating bead plate layer and the aluminum silicate fiber cotton layer can be taken as a whole according to the composite layer structural characteristics of the first composite plate, and finite element analysis software (such as ANSYS, COMSOL, etc.) can be used to establish a three-dimensional grid unit including the floating bead plate layer and the aluminum silicate fiber cotton layer, and the thermal conductivity, density and specific heat capacity of the floating bead plate layer, as well as the thermal conductivity, density and specific heat capacity of the aluminum silicate fiber cotton layer are entered into the grid unit attribute table; then, the external heat source temperature and the ambient temperature are set as the first type of boundary conditions, and the convection heat transfer coefficient is set as the third type of boundary condition, and a heat conduction equation is established in each grid unit of the three-dimensional grid unit, and the equation is: , ρ represents the medium density (kg / m 3 ), c p represents specific heat capacity (J / kg·K), T represents temperature (K), and t represents time (s). represents the gradient operator, k represents the thermal conductivity (W / m·K), Q represents the internal heat source per unit volume (W / m 3), if there is no internal heat source, then Q = 0; then, the finite volume method is used to discretize and solve the heat conduction equation, and the corresponding temperature field distribution is obtained. The entire computational domain (i.e., three-dimensional grid unit) is divided into multiple small control volumes (i.e., multiple grid units). For any control volume (denoted as the i-th control volume), the heat conduction equation within it can be discretized and expressed as: , V i represents the volume of the i-th control volume, T i n represents the temperature of the ith control body at the nth time step, △t represents the time step (i.e., the time difference between the n+1th time step and the nth time step), f represents the adjacent control body of the ith control body, k f and A f denote the thermal conductivity and interface area between the ith control volume and the adjacent control volume f, respectively. f represents the temperature of the adjacent control volume f, T i represents the temperature of the i-th control body, △x f represents the characteristic length (such as grid spacing) from the i-th control volume to the adjacent control volume f, Q i represents the heat source term in the i-th control volume. Furthermore, the relative change rate of the temperature field is set to be less than 0.001 as the convergence criterion, that is, the iteration is stopped when the maximum temperature change between two consecutive iterations meets the following conditions: After the iteration stops, the stable temperature field distribution T(x, y, z) can be obtained, (x, y, z) represents the spatial coordinates; then, according to the obtained temperature field distribution T(x, y, z), the corresponding temperature gradient can be obtained by the central difference method. For the three-dimensional case, the temperature gradient It can be expressed as: Finally, the temperature gradient can be calculated based on , a heat flow line tracing algorithm is used to identify the main heat conduction path in the three-dimensional grid unit and obtain the heat flux density on the main heat conduction path. The heat flow line is a curve starting from a point and extending along the direction of the local temperature gradient. Starting from the initial point, it gradually moves forward in the direction of the local temperature gradient until it reaches the boundary or other specified end point. Along the main heat conduction path, the heat flow line can be calculated according to the formula The corresponding heat flux q is calculated at each point on the path.

[0076] It should be noted that the heat conduction characteristics of the composite layer structure directly affect its fire resistance. Taking the fireproof and soundproof barrier (i.e. composite board) of a certain substation as an example, the thermal conductivity coefficient of the floating bead board layer is 0.065W / m·K and the density is 400kg / m 3, the specific heat capacity is 840J / kg·K, the thermal conductivity of the aluminum silicate fiber cotton layer is 0.035W / m·K, and the density is 120kg / m 3 , the specific heat capacity is 1200 J / kg·K; when establishing the three-dimensional grid unit, the grid size is set to 2 mm, and the total number of grids reaches 120,000; in the boundary condition setting, the external heat source temperature is set to 850℃, the ambient temperature is 25℃, and as the first type of boundary condition, the external convection heat transfer coefficient is set to 25W / (m 2 ·K) as the third-type boundary condition; the initial temperature field calculation uses a time step of 0.1s, and a heat conduction equation containing conduction and convection terms is established in each grid cell. When solving with the finite volume method, the central difference format is used to discretize the spatial terms, and the implicit format is used to discretize the time terms. The relative rate of change of the temperature field is set to be less than 0.001 as the convergence criterion; the calculation results show that under steady-state conditions, the maximum temperature of the outer surface of the composite layer structure is 780℃, and the maximum temperature of the inner surface is 120℃; through heat streamline tracing, it is found that there are three main heat transfer channels in the composite layer structure, located in the upper, middle and lower regions respectively; the heat flux density distribution calculation shows that the maximum heat flux density on the outer surface reaches 15kW / m on the main heat conduction path. 2 At the interface between the floating bead layer and the aluminum silicate fiber cotton layer, the heat flux density is reduced to 8kW / m due to the existence of contact thermal resistance. 2 .

[0077] It should be noted that the fire performance evaluation adopts the heat flux density threshold method, and the preset fire threshold is 10kW / m 2 The analysis results show that under the action of an external heat source at 850°C, the heat flux density of only 15% of the outer surface exceeds the preset fire protection threshold, and these areas are mainly concentrated in the surface positions directly heated. In the interior of the composite layer structure, thanks to the interface contact thermal resistance and the low thermal conductivity of the dielectric material itself, the heat flux density is lower than the preset fire protection threshold, indicating that the composite layer structure has good fire protection performance.

[0078] In one optional embodiment, the second physical parameter includes the porosity of the floating bead layer and the thickness of the aluminum silicate fiber cotton layer;

[0079] Then, optimizing the second physical parameter of the first composite plate to obtain the second composite plate specifically includes:

[0080] Marking areas on the heat conduction path where the heat flux density exceeds a preset fire protection threshold, and obtaining porosity adjustment amounts of the floating bead board layer and thickness adjustment amounts of the aluminum silicate fiber cotton layer corresponding to the areas;

[0081] updating the three-dimensional grid unit of the first composite plate according to the porosity adjustment amount and the thickness adjustment amount, wherein the grid size of the updated three-dimensional grid unit is determined by the minimum characteristic size of the floating bead plate layer and the aluminum silicate fiber cotton layer;

[0082] Identify a new heat conduction path based on the updated three-dimensional grid unit and obtain a new heat flux density on the new heat conduction path;

[0083] Determine whether the new heat flux density exceeds the preset fire protection threshold. If so, re-optimize the second physical parameter of the first composite board until the new heat flux density obtained does not exceed the preset fire protection threshold. If not, optimize the porosity of the floating bead board layer and the thickness of the aluminum silicate fiber cotton layer according to the currently obtained porosity adjustment amount and thickness adjustment amount to obtain a second composite board.

[0084] Specifically, in combination with the above embodiment, the second physical parameter of the first composite plate includes the porosity of the floating bead board layer and the thickness of the aluminum silicate fiber cotton layer. Accordingly, when actually optimizing the second physical parameter of the first composite plate, the heat flow line tracing method can be used on the identified heat conduction path to mark the area where the heat flux density exceeds the preset fire protection threshold, and the porosity adjustment amount of the floating bead board layer and the thickness adjustment amount of the aluminum silicate fiber cotton layer corresponding to the marked area can be obtained. For example, assuming that the initial porosity of the floating bead board layer is P0, a reasonable adjustment ratio is determined through experiments or empirical data, preferably 1.2, then the porosity adjustment amount is: △P=(1.2-1)×P0=0.2×P0, assuming that the initial thickness of the aluminum silicate fiber cotton layer is d0, in order to reduce the heat flux density to below the preset fire protection threshold, the thickness needs to be increased. The required increase in thickness (i.e., the thickness adjustment amount) can be estimated by the thermal resistance formula R=d / (k×A): △d=(R target -R0)×k×A,R targetRepresents the target thermal resistance, R0 represents the initial thermal resistance, k represents the thermal conductivity, and A represents the heat transfer area; according to the obtained porosity adjustment amount of the floating bead layer and the thickness adjustment amount of the aluminum silicate fiber cotton layer, the new porosity of the floating bead layer and the new thickness of the aluminum silicate fiber cotton layer can be obtained, and the three-dimensional grid unit of the first composite plate established in the above embodiment is updated based on the new porosity and new thickness, and the updated three-dimensional grid unit is obtained accordingly, wherein the grid size of the updated three-dimensional grid unit is determined by the minimum characteristic size of the floating bead layer and the aluminum silicate fiber cotton layer (for example, the pore size of the floating bead layer or the fiber diameter of the aluminum silicate fiber cotton layer) to ensure that the updated grid size can adapt to the changes in the properties of the medium material. For example, for high-precision simulation, the grid size can usually be set to 1 / 4 to 1 / 6 of the minimum characteristic size; thereafter, the new heat conduction path can be identified based on the updated three-dimensional grid unit, and the new heat flux density on the new heat conduction path can be obtained. For example, the steady-state heat conduction equation is applied to the updated three-dimensional grid unit. , combined with the boundary conditions to perform numerical solution, and again according to the formula The corresponding heat flux density q is calculated at each point on the path (the specific calculation process is similar to the above embodiment and will not be repeated here); further, the currently obtained new heat flux density is compared with the preset fire protection threshold to determine whether the first composite plate after the current second physical parameter is updated meets the fire protection requirements. If it is determined that the currently obtained new heat flux density still exceeds the preset fire protection threshold, it means that the first composite plate after the current second physical parameter is updated still does not meet the fire protection requirements, and it is necessary to return to re-optimize the second physical parameter of the first composite plate (that is, re-execute the optimization process of the second physical parameter of this embodiment) until it is determined that the currently obtained new heat flux density does not exceed the preset fire protection threshold. Accordingly, if it is determined that the currently obtained new heat flux density does not exceed the preset fire protection threshold, the porosity of the floating bead board layer and the thickness of the aluminum silicate fiber cotton layer of the first composite plate are adjusted according to the currently obtained porosity adjustment amount of the floating bead board layer and the thickness adjustment amount of the aluminum silicate fiber cotton layer, and the second composite plate is obtained accordingly.

[0085] For example, if the pore size distribution of the floating bead layer is between 0.1 and 0.5 mm, the grid size of the updated three-dimensional grid unit can be set to 0.025 mm (i.e., 1 / 4 of 0.1 mm); if the fiber diameter of the aluminum silicate fiber cotton layer is 0.03 mm, the grid size of the updated three-dimensional grid unit can be set to 0.0075 mm (i.e., 1 / 4 of 0.03 mm).

[0086] It should be noted that in the optimization of the fire protection performance of the composite layer structure, the treatment of heat flux density exceeding the preset fire protection threshold involves multiple key links. Taking the fire and sound insulation barrier of a certain substation as an example, when the heat flux density on the outer surface reaches 12kW / m2 , more than 10kW / m 2 When the fire threshold is exceeded, the composite layer structure needs to be adjusted and optimized. Through heat flow tracing, it was found that the area with excessive heat flux density is mainly concentrated in the central area, covering an area of ​​about 25% of the total area. For the areas with excessive heat flux density, a zoned thickening treatment method was adopted. In terms of optimizing the thickness of the aluminum silicate fiber cotton layer in the excessive area, the thickness was increased from the original 50mm to 75mm, with a thickening coefficient of 1.5. The thermal insulation performance calculation after thickening showed that the thermal resistance value of this area increased from the original 1.4K·m² / W to 2. 1K·m² / W, and the heat flow conduction time is extended by 45%; in terms of optimizing the porosity of the floating bead plate layer, the density gradient method is used to adjust the corresponding thickened area, and the porosity is increased from the original 45% to 54%, with an increase coefficient of 1.2. After the porosity is increased, the thermal conductivity of the floating bead plate layer is reduced to 0.052W / m·K, while the compressive strength is still maintained at above 2.8MPa, meeting the structural compressive strength requirements; after the structural adjustment, the interface heat transfer characteristics need to be recalculated, and the contact heat transfer coefficient of the adjusted area is increased from the original 80W / (m 2 K) is reduced to 65W / (m 2 K), which is caused by the change in the interfacial contact pressure of the dielectric material.

[0087] Furthermore, in the meshing of the new three-dimensional grid cells, the grid size of the thickened area can be kept unchanged at 2 mm, but the number of grids is increased to 150,000. The steady-state thermal conductivity calculation results show that the maximum heat flux density of the composite layer structure after parameter adjustment is reduced to 9.5 kW / m under the action of an external heat source at 850 ° C. 2 , below the fire protection threshold of 10kW / m 2 The temperature field distribution shows an obvious step-like shape, with the maximum temperature of the outer surface dropping to 720°C and the maximum temperature of the inner surface dropping to 95°C.

[0088] It is understandable that in practical applications, parameter optimization of composite layer structures often requires multiple rounds of iterations. If the heat flux density in some local areas still exceeds the fire protection threshold after the first round of optimization, a second round of optimization is required. Similarly, as the number of iterations increases, the thickness increase coefficient of the aluminum silicate fiber cotton layer and the porosity increase coefficient of the floating bead board layer are usually appropriately increased; through this step-by-step optimization method, the fire protection performance of the composite layer structure meets the design requirements; optimization experience shows that the thickening coefficient should not exceed 2.0, and the porosity increase coefficient should not exceed 1.5, otherwise it will affect the overall performance of the composite layer structure.

[0089] Step S15: obtaining a noise penetration index of the target low-frequency noise in the second composite panel, and when the noise penetration index does not exceed a preset penetration threshold, optimizing a third physical parameter of the second composite panel to obtain a third composite panel.

[0090] It should be noted that after obtaining the second composite panel through parameter adjustment, the noise penetration index of the target low-frequency noise inside the second composite panel can be further obtained, and the obtained noise penetration index can be compared with the penetration threshold to determine whether the second composite panel meets the noise reduction requirements. If it is determined that the noise penetration index does not exceed the preset penetration threshold, it means that the noise reduction requirements are not met at this time, and it is necessary to optimize the third physical parameter of the second composite panel, and obtain the composite panel with the optimized third physical parameter accordingly, and use it as the third composite panel.

[0091] It should be noted that, in combination with the following embodiments, the noise penetration index finally obtained includes the noise penetration index at different incident angles. As long as the noise penetration index at any incident angle does not exceed the preset penetration threshold, the third physical parameter of the second composite plate needs to be optimized.

[0092] In one optional embodiment, obtaining the noise penetration index of the target low-frequency noise in the second composite plate specifically includes:

[0093] According to the structure of the second composite plate, the acoustic grid unit is divided using the quarter-wavelength criterion, and the acoustic parameters of the floating bead plate layer and the aluminum silicate fiber cotton layer are added, wherein the acoustic parameters include density distribution, sound velocity distribution, and acoustic impedance distribution;

[0094] Setting the incident angle range and frequency range of the sound wave, and for each combination of incident angle and frequency, using a sound wave tracking algorithm to simulate the propagation path of the target low-frequency noise in the acoustic grid unit;

[0095] Calculating the attenuation of the target low-frequency noise during propagation based on the material sound absorption coefficient, wherein the material sound absorption coefficient is determined by the density distribution and sound velocity distribution of the floating bead board layer and the aluminum silicate fiber cotton layer;

[0096] According to the acoustic impedance distribution of the floating bead board layer and the aluminum silicate fiber cotton layer, the reflection coefficient and the transmission coefficient of the target low-frequency noise at the interface are calculated using the transfer matrix method;

[0097] Acquiring a time-domain sound pressure signal on the propagation path according to the attenuation, the reflection coefficient, and the transmission coefficient;

[0098] Performing a fast Fourier transform on the time-domain sound pressure signal to obtain a frequency-domain sound pressure spectrum;

[0099] According to the formula N P =|Pout| / |Pin| Calculate the noise penetration index of the target low-frequency noise in the second composite board, N Prepresents the noise penetration index, Pin and Pout represent the input sound pressure of one surface and the output sound pressure of the other surface of the second composite plate respectively, and Pin and Pout are obtained according to the frequency domain sound pressure spectrum.

[0100] Specifically, in combination with the above embodiment, when obtaining the noise penetration index of the target low-frequency noise in the second composite plate, the acoustic grid unit can be divided according to the structure of the second composite plate using the quarter-wavelength criterion, the grid size can be set to one-quarter of the wavelength of the sound wave corresponding to the highest frequency, and the acoustic parameters of the bead board layer and the aluminum silicate fiber cotton layer can be recorded, and the acoustic parameters include density distribution, sound velocity distribution and acoustic impedance distribution; then the incident angle range (for example, from 0° to 60°) and frequency range (for example, from 50Hz to 200Hz) of the sound wave are set, and for each combination of incident angles and frequencies, the sound wave tracking algorithm is used to simulate the propagation path of the target low-frequency noise in the acoustic grid unit of the second composite plate, and the change in the sound pressure signal amplitude at each grid node is recorded; then, the attenuation of the target low-frequency noise during the propagation process is calculated based on the sound absorption coefficient of the material, wherein the sound absorption coefficient of the material (denoted as α) is the sound absorption coefficient of the bead board. The total sound absorption coefficient of the layer and the aluminum silicate fiber cotton layer is determined by the density distribution and sound velocity distribution of the floating bead board layer and the density distribution and sound velocity distribution of the aluminum silicate fiber cotton layer. The attenuation is calculated as follows: Attenuation = 10×log(1-α). For example, considering the multi-layer effect, the material sound absorption coefficient is equal to the sum of the sound absorption coefficients of the floating bead board layer and the aluminum silicate fiber cotton layer minus the product of the two. Assuming that at a frequency of 100 Hz, the sound absorption coefficient of the floating bead board layer is 0.3 and the sound absorption coefficient of the aluminum silicate fiber cotton layer is 0.5, then the material sound absorption coefficient is: α=(0.3+0.5)-0.3×0.5=0.65; then, according to the acoustic impedance distribution of the floating bead board layer and the acoustic impedance distribution of the aluminum silicate fiber cotton layer, the transfer matrix method is used to calculate the reflection coefficient and transmission coefficient of the target low-frequency noise at the interface. Assuming that the acoustic impedance of the floating bead board layer is Z1 and the acoustic impedance of the aluminum silicate fiber cotton layer is Z2, then the reflection coefficient is calculated as follows: , the transmission coefficient is calculated as: ; Then, the sound pressure signal amplitude at each grid node is updated according to the calculated attenuation (the attenuation effect is taken into account to correct the energy loss of the sound pressure signal), and the reflection and transmission of the sound wave at the interface are processed according to the calculated reflection coefficient and transmission coefficient to correct the sound pressure signal amplitude when the sound wave passes through the interface, and finally the time domain sound pressure signal on the entire propagation path (that is, the corrected sound pressure signal) is obtained; thereafter, the time domain sound pressure signal obtained at each frequency point can be fast Fourier transformed to obtain the frequency domain sound pressure spectrum accordingly; finally, the Pin and Pout corresponding to the target low-frequency band noise passing through the second composite plate can be obtained according to the obtained frequency domain sound pressure spectrum, where Pin represents the input sound pressure of one surface of the second composite plate (usually the front surface of the second composite plate, from which the target low-frequency band noise enters), and Pout represents the output sound pressure of the other surface of the second composite plate (usually the rear surface of the second composite plate, from which the target low-frequency band noise is output). Based on the determined Pin and Pout, the formula N P =|Pout| / |Pin|Calculate the noise penetration index N of the target low-frequency noise in the second composite board P .

[0101] It should be noted that the acoustic performance evaluation of the composite layer structure requires accurate simulation of the sound wave propagation process. In the division of the acoustic grid unit, it can be divided according to the quarter-wavelength criterion, and the grid size can be set to one-quarter of the sound wave wavelength corresponding to the highest frequency. For example, the highest frequency of the target low-frequency noise is 200Hz, and the corresponding sound wave wavelength is 1.7 meters, then the grid size can be set to 0.425 meters; for the floating bead board layer, the material density is 400kg / m 3 , the longitudinal wave speed is 2500m / s, and the calculated acoustic impedance is 1MPa·s / m; for the aluminum silicate fiber cotton layer, the material density is 120kg / m 3 The longitudinal wave velocity is 1800m / s, and the acoustic impedance is 0.216MPa·s / m. Regarding the acoustic wave incident condition setting, the incident angle starts at 0° and is incremented every 15° up to 60°, forming five incident angles. Within the frequency range, a frequency point is taken every 25Hz, resulting in seven characteristic frequencies. This parameter setting scheme ensures comprehensive characterization of the acoustic wave propagation characteristics. When the target low-frequency noise propagates in the composite layer structure, the acoustic pressure changes along the propagation path can be recorded using the acoustic wave tracking algorithm. Taking a 90dB, 100Hz sound wave incident vertically as an example, the sound pressure level decreases by 15dB when passing through the beadboard layer during propagation, of which material absorption loss accounts for 12dB and interface reflection loss accounts for 3dB. The sound pressure level further decreases by 22dB when passing through the aluminum silicate fiber cotton layer, of which material absorption loss accounts for 18dB and interface loss accounts for 4dB.

[0102] Furthermore, in the analysis of the interface acoustic characteristics, the acoustic impedance ratio of the two layers of material was 4.63. The reflection coefficient at the interface was calculated to be 0.35 and the transmission coefficient was 0.65 using the transfer matrix method. This indicates that at this frequency, 35% of the sound energy is reflected at the interface, while 65% of the sound energy continues to propagate. The sound pressure signal was analyzed in the frequency domain using fast Fourier transform. The sound energy conversion characteristics showed significant differences at different incident angles. When the incident angle increased from 0° to 60°, the interface reflection coefficient gradually increased. At an incident angle of 45°, the reflection coefficient of a 100Hz sound wave increased to 0.48, while the transmission coefficient decreased to 0.52. In the frequency range of 50 to 200Hz, the average transmission coefficient at vertical incidence was 0.42, while the average transmission coefficient at 60° incidence decreased to 0.25. This indicates that the composite layer structure has better sound insulation effect for oblique incident sound waves.

[0103] In one optional embodiment, the third physical parameter includes the porosity and pore size of the floating bead layer and the density of the aluminum silicate fiber cotton layer;

[0104] Then, optimizing the third physical parameter of the second composite plate to obtain the third composite plate specifically includes:

[0105] Obtaining a third set of physical parameters according to the adjustment range and adjustment amplitude of the porosity of the floating bead layer, the adjustment range and adjustment amplitude of the pore size, and the adjustment range and adjustment amplitude of the density of the aluminum silicate fiber cotton layer;

[0106] Using sensitivity analysis or partial derivative method, calculate the influence weights of the porosity and pore size of the floating bead layer and the density of the aluminum silicate fiber cotton layer on the acoustic impedance;

[0107] According to the influence weight and in accordance with the acoustic impedance matching principle, the third physical parameters in the third physical parameter set are graded and screened to establish a response relationship between the acoustic impedance and the third physical parameters;

[0108] Using an iterative optimization algorithm to obtain an optimal parameter combination of each third physical parameter in the third physical parameter set;

[0109] The porosity and pore size of the floating bead board layer and the density of the aluminum silicate fiber cotton layer are optimized according to the optimal parameter combination to obtain a third composite board.

[0110] Specifically, in combination with the above embodiment, the third physical parameter of the second composite board includes the porosity and pore size of the floating bead board layer and the density of the aluminum silicate fiber cotton layer. Accordingly, when optimizing the third physical parameter of the second composite board, the adjustment range of the porosity P of the floating bead board layer (i.e., the porosity reference value P) can be set. bThe floating range of up and down fluctuations, such as [0.3, 0.6]) and the adjustment amplitude (ie, the adjustment step), the adjustment range of the aperture Φ of the floating bead layer (ie, the aperture reference value Φ b The floating range of up and down fluctuations, such as [10, 100], in μm) and the adjustment amplitude (i.e., the adjustment step), the density of the aluminum silicate fiber cotton layer ρ f The adjustment range (i.e. the density reference value ρ fb The floating range of up and down fluctuations, for example [150, 250], the unit is kg / m 3 ) and the adjustment amplitude (i.e., the adjustment step), and the corresponding third physical parameter set is obtained; in order to quantify each third physical parameter (porosity P, pore size Φ, density ρ f ) on the acoustic impedance Z, sensitivity analysis or partial derivative method can be used to calculate the porosity P, pore size Φ of the floating bead layer and the density ρ of the aluminum silicate fiber cotton layer. f The influence weight on the acoustic impedance Z, where it is assumed that the acoustic impedance Z is a function of each third physical parameter, and the function is Z=Z(P, Φ, ρ f ), which can be obtained by fitting theoretical models (such as the Biot model or porous material acoustic model) or experimental data, then, for each third physical parameter, its partial derivative is calculated, which is: , thereby obtaining the influence weight of each third physical parameter on the acoustic impedance; then, according to the obtained influence weight corresponding to each third physical parameter, in accordance with the acoustic impedance matching principle, each third physical parameter in the third physical parameter set is graded and screened (combined with the adjustment range and adjustment amplitude corresponding to each third physical parameter) to establish a response relationship between the acoustic impedance and each third physical parameter; then, based on the established response relationship between the acoustic impedance and each third physical parameter, an iterative optimization algorithm is used to obtain the optimal parameter combination corresponding to each third physical parameter in the third physical parameter set; finally, according to the obtained optimal parameter combination, the porosity and pore size of the floating bead board layer and the density of the aluminum silicate fiber cotton layer are adjusted respectively to obtain the third composite board accordingly.

[0111] It should be noted that the acoustic impedance matching principle requires that the acoustic impedance of the composite layer structure be as close as possible to that of the surrounding medium (for example, the acoustic impedance of air is Zair≈415Pa·s / m) to minimize the reflection coefficient. , that is, the hierarchical screening is achieved by adjusting the third physical parameters (P, Φ, ρ f ), so that the acoustic impedance of the composite layer structure after parameter adjustment is close to the target value Z target (such as Z target=Zair), specifically, according to the weight impact analysis, the third physical parameter with the largest weight impact (such as porosity P) can be adjusted first, followed by the third physical parameter with a medium weight impact (such as pore size Φ), and finally the third physical parameter with the smallest weight impact (such as density ρ f ), then, first, fix Φ=Φ b ,ρ f =ρ fb , within the range of P∈[0.3,0.6], adjust P multiple times according to the adjustment step of P, and calculate Z(P) after each adjustment, and select the one that makes |ZZ target |The smallest P subset, then, for the selected P subset, within the range of Φ∈[10,100], adjust Φ multiple times according to the adjustment step of Φ, and calculate Z(P,Φ) after each adjustment, further select the (P,Φ) subset that meets the conditions, and finally, for the selected (P,Φ) subset, in ρ f ∈[150, 250], according to ρ f The adjustment step size is adjusted multiple times f , and calculate each adjusted Z(P, Φ, ρ f ), further screen out (P, Φ, ρ f ), as a candidate parameter set; for the obtained candidate parameter set, the response relationship between the acoustic impedance and each third physical parameter is fitted through numerical simulation or experimental data: Z=Z(P, Φ, ρ f ), correspondingly, the fitting is , a1, a2, and a3 are fitting coefficients of each third physical parameter, and the values ​​of the fitting coefficients can be determined by experiments or simulations (such as COMSOL Multiphysics).

[0112] It should be noted that when the iterative optimization algorithm is used to obtain the optimal parameter combination of each third physical parameter in the third physical parameter set, the optimization goal is to minimize the acoustic impedance Z and the target value Z target Deviation between: minJ=|Z(P,Φ,ρ f )-Z target |, for the third physical parameter set, a gradient descent method or a genetic algorithm can be used to calculate the reference value (P b , Φ b , ρ fb ) starts iterative update, and each iteration can update (P, Φ, ρ) according to the gradient information f ), for example, assuming that i is the current iteration, the third physical parameters in the next iteration (i.e., the i+1th iteration) can be updated as follows: , η is the learning rate, preferably, η=0.01; set the acoustic impedance change rate of adjacent iteration cycles as the convergence criterion, and set the threshold as the convergence condition (such as ΔZ rate <0.01), after each iteration, it is determined whether the convergence condition is met. If so, the iteration is stopped and the optimal parameter combination is obtained accordingly.

[0113] For example, taking a substation fire and sound insulation barrier as an example, the porosity of the floating bead layer in the initial state is 45%, the pore size distribution is in the range of 0.2 to 0.5 mm, and the density of the aluminum silicate fiber cotton layer is 120 kg / m 3 The measured noise penetration index is 0.35, which is lower than the preset penetration threshold of 0.4. In the parameter influence weight analysis, the single factor change method found that when the porosity of the floating bead layer increases by 5%, the acoustic impedance decreases by about 8%. When the pore size of the floating bead layer increases by 0.1mm, the acoustic impedance decreases by about 5%. When the density of the aluminum silicate fiber cotton layer increases by 20kg / m 3 , the acoustic impedance is increased by about 12%; based on these response relationships, the adjustment step of porosity is set to 2%, the adjustment step of pore size is set to 0.05mm, and the adjustment step of the density of the aluminum silicate fiber cotton layer is set to 10kg / m 3 The acoustic impedance optimization process adopts an iterative calculation method. The acoustic impedance change rate is calculated in each iteration. When the acoustic impedance change rate of two consecutive iterations is less than 1% (that is, satisfying ΔZ rate <0.01). The optimization calculation shows that the porosity of the floating bead layer is increased to 52%, the pore size is adjusted to the range of 0.15 to 0.4 mm, and the density of the aluminum silicate fiber cotton layer is increased to 145 kg / m 3 When , the acoustic impedance matching of the composite layer structure reaches the optimal value.

[0114] Step S16: Acquire the acoustic impedance distribution and thermal conductivity distribution of the third composite plate, and determine the final physical parameters of the third composite plate corresponding to the acoustic-thermal performance balance point according to the acoustic impedance distribution and the thermal conductivity distribution, so as to adjust the structure of the third composite plate.

[0115] In one optional embodiment, obtaining the acoustic impedance distribution and thermal conductivity distribution of the third composite plate, and determining the final physical parameters of the third composite plate corresponding to the acoustic-thermal performance balance point according to the acoustic impedance distribution and the thermal conductivity distribution to adjust the structure of the third composite plate specifically includes:

[0116] Establishing an acoustic grid unit of the third composite plate, and calculating the acoustic impedance of each grid unit to obtain an acoustic impedance distribution of the acoustic grid unit;

[0117] According to the acoustic impedance distribution, obtaining the thermal conductivity distribution of the acoustic grid unit through heat conduction simulation or experimental measurement;

[0118] Establishing an acoustic-thermal performance coupling matrix according to the acoustic impedance distribution and the thermal conductivity distribution;

[0119] Set a normalized noise reduction index and a fire prevention index, and establish an acoustic thermal performance function based on the noise reduction index and the fire prevention index. The acoustic thermal performance function is f(N, F)=w N ×(1-N)+w F ×F, N represents the noise reduction index, w N represents the weight of the noise reduction index, F represents the fire prevention index, w F Indicates the weight of the fire protection index;

[0120] Within a preset weight value range, an iterative optimization algorithm is used to determine the optimal weight combination of the noise reduction index and the fire protection index in the acoustic and thermal performance function to determine the acoustic and thermal performance balance point. The acoustic and thermal performance coupling matrix is ​​used to evaluate the comprehensive impact of the adjustment of the porosity and pore size of the floating bead board layer and the density of the aluminum silicate fiber cotton layer on the acoustic and thermal performance during the iterative optimization process;

[0121] The porosity, pore size and density of the aluminum silicate fiber cotton layer of the floating bead board layer corresponding to the acoustic and thermal performance balance point are obtained, so as to adjust the structure of the third composite board according to the porosity, pore size and density of the aluminum silicate fiber cotton layer of the floating bead board layer corresponding to the acoustic and thermal performance balance point.

[0122] Specifically, in combination with the above embodiment, when obtaining the acoustic impedance distribution and thermal conductivity distribution of the third composite plate, an acoustic grid unit of the third composite plate can be established first. For example, the acoustic grid unit is established using the finite element method, and the grid size is set to 1mm×1mm×1mm. The corresponding third physical parameters (P, Φ, ρ f ), and calculate the acoustic impedance of each grid node. The acoustic impedance at the i-th grid node is expressed as: Z i =Z(P i , Φ i , ρ f_i ) to obtain the acoustic impedance distribution of the entire acoustic grid unit, and then obtain the thermal conductivity distribution of the entire acoustic grid unit through heat conduction simulation or experimental measurement based on the obtained acoustic impedance distribution.

[0123] Further, in combination with the above embodiment, when determining the final physical parameters of the third composite plate corresponding to the acoustic-thermal performance balance point based on the acoustic impedance distribution and the thermal conductivity distribution, an acoustic-thermal performance coupling matrix can be established based on the obtained acoustic impedance distribution and thermal conductivity distribution, wherein the acoustic impedance Z and the thermal conductivity λ are respectively used as the row parameter and column parameter of the acoustic-thermal performance coupling matrix, and the coupling coefficient = ΔZ / Δλ is defined to characterize the degree of mutual influence between the acoustic and thermal performances; then, a normalized noise reduction index N∈[0,1] and a fire prevention index F∈[0,1] are set, and an acoustic-thermal performance function is established based on the noise reduction index N and the fire prevention index F, and the acoustic-thermal performance function is f(N,F)=w N ×(1-N)+w F ×F,w N Represents the weight of the noise reduction index N, w F Represents the weight of the fire protection index F; then, when w is satisfied N +w F =1, pre-set w N The value range and w F The optimal weight combination of the noise reduction index N and the fire protection index F in the acoustic thermal performance function f(N, F) is determined by an iterative optimization algorithm within the preset weight value range, and the acoustic thermal performance coupling matrix is ​​used to evaluate each third physical parameter (P, Φ, ρ f ) on the acoustic and thermal performance, that is, by adjusting w N and w F , and calculate each set of adjusted w N and w F Corresponding to f(N, F), select a set of w that maximizes the value of f(N, F) N and w F As the optimal weight combination, and recorded as (w N_m , w F_m ), it can be understood that when the value of f(N, F) reaches the maximum, the acoustic and thermal performance reaches the optimal balance point; finally, the third physical parameters under the acoustic and thermal performance balance point are obtained and recorded as (P m , Φ m , ρ f_m ), and according to (P m , Φ m , ρ f_m ) respectively adjust the porosity, pore size and density of the aluminum silicate fiber cotton layer of the third composite board to adjust the structure of the third composite board.

[0124] It should be noted that the collaborative optimization of acoustic and thermal performance involves complex parameter balance issues. Taking the noise reduction and fire protection system of a certain substation as an example, the acoustic and thermal performance coupling matrix is ​​established for analysis, and the noise reduction index includes noise penetration index, sound pressure attenuation, and frequency response characteristics, and their value ranges are normalized to the interval of 0 to 1. The fire protection index includes heat flux density, temperature gradient, and flame retardant time, which are also normalized to the interval of 0 to 1. In the weight coefficient allocation, a hierarchical progressive method can be used to determine the optimal weight combination: in the initial state, the noise reduction weight w N With fire weight w F are all set to 0.5. Through iterative calculation, it is found that when the noise reduction weight w N is 0.6, fire protection weight w F When the value is 0.4, the acoustic and thermal performance reaches the optimal balance point. At this balance point, the noise penetration index is 0.42 and the heat flux density is 8.5kW / m 2 , both indicators meet the design requirements.

[0125] In other optional embodiments, the method further includes:

[0126] After adjusting the structure of the third composite panel, monitoring the noise reduction and fire prevention operation status of the substation;

[0127] The monitoring of the noise reduction and fire prevention operation status of the substation specifically includes:

[0128] Collect noise and temperature data of substations according to preset sampling periods;

[0129] Using a multi-source data fusion method, an acoustic-thermal performance evaluation index including a plurality of acoustic characteristics and a plurality of thermal characteristics is obtained based on the noise data and the temperature data;

[0130] Inputting the acoustic and thermal performance evaluation index into a trained deep neural network for prediction to obtain an acoustic and thermal performance warning probability;

[0131] According to the preset warning level, determining whether the acoustic and thermal performance warning probability reaches the warning standard;

[0132] If so, a warning signal of the corresponding level is triggered and a real-time monitoring status report is output.

[0133] Specifically, in combination with the above embodiment, according to (P m , Φ m , ρ f_m) After adjusting the structure of the third composite plate, the noise reduction and fire prevention operation status of the substation can be monitored in real time based on the adjusted third composite plate. In specific implementation, the noise data and temperature data of the substation during operation can be collected according to a preset sampling period, wherein the data sampling period and storage format can be set according to the equipment characteristics to ensure the time alignment of noise data and temperature data from different sources; then, based on the currently collected noise data and temperature data, a multi-source data fusion method is used to extract several acoustic features and several thermal features to obtain the current acoustic and thermal performance evaluation index of the substation; thereafter, the obtained current acoustic and thermal performance evaluation index of the substation is input into the trained deep neural network for prediction, and the current acoustic and thermal performance warning probability of the substation is obtained accordingly; finally, according to the preset warning level classification standard, it is judged whether the predicted current acoustic and thermal performance warning probability of the substation meets the warning standard. If it is determined that the warning standard is met, the warning signal of the corresponding warning level is triggered, and a real-time monitoring status report is output.

[0134] For example, the monitoring sensor network can be arranged in a grid layout, with 8 noise sensors, 12 temperature sensors, and 4 load sensors arranged at key locations of the substation; the data sampling period can be set to 1 minute, and each sensor generates 1440 data points per day.

[0135] It should be noted that the noise data at least includes the noise decibel value, the temperature data at least includes the surface temperature, and the acoustic features extracted by the multi-source data fusion method at least include: equivalent continuous sound level ( , T is the total measurement time, p(t) is the instantaneous sound pressure at time t), sound pressure peak value and spectrum center of gravity ( , M is the total number of frequency points, f i is the frequency of the ith frequency point, A(f i ) is the sound pressure amplitude at the ith frequency point), thermal characteristics include at least: maximum surface temperature ( , T1, T2, ... are the real-time readings of each temperature sensor), temperature gradient ( , T(x+Δx) is the temperature value at position x+Δx, T(x-Δx) is the temperature value at position x-Δx, Δx is a small distance step along the x direction, used to discretize the space, 2Δx is the interval from position x+Δx to position x-Δx) and heat flux density; further, the embodiment of the present invention can pre-establish an acoustic and thermal performance evaluation index system for the substation based on several acoustic characteristics and several thermal characteristics. In the actual monitoring process, the corresponding operating characteristic values ​​can be extracted according to the noise data and temperature data collected in real time, and real-time acoustic and thermal performance evaluation indicators can be obtained based on the acoustic and thermal performance evaluation index system.

[0136] It should be noted that for the trained deep neural network, its input dimension is the number of features contained in the acoustic and thermal performance evaluation index. The hidden layer uses multiple fully connected layers, and each fully connected layer is followed by an activation function (such as ReLU, etc.). Taking the hidden layer using two fully connected layers as an example: the output vector of the first hidden layer is: H1=ReLU(W1X+b1), the output vector of the second hidden layer is: H2=ReLU(W2H1+b2), X is the feature vector corresponding to the acoustic and thermal performance evaluation index of the input deep neural network, W1 and W2 are the weight matrices of the first hidden layer and the second hidden layer respectively, b1 and b2 are the bias terms of the first hidden layer and the second hidden layer respectively, and the output layer of the deep neural network finally outputs a scalar, which represents the acoustic and thermal performance warning probability, and the acoustic and thermal performance warning probability = σ(W o H2+b o ), σ is the Sigmoid activation function to ensure that the output is between 0 and 1, W o is the weight matrix of the output layer, H2 is the output vector of the previous hidden layer (i.e. the second hidden layer), b o is the bias term of the output layer; further, the embodiment of the present invention can collect a large amount of historical data in advance to train the deep neural network model and mark the normal state and abnormal state. For example, the label Y (Y=1 indicates abnormality, Y=0 indicates normal) can be used for marking, and the cross entropy loss function (Binary Cross-Entropy Loss) can be used as the loss function in the model training process. The expression of the cross entropy loss function is: , N is the number of training samples, i is the sample index, yi is the true label of the i-th training sample, is the predicted probability of the i-th training sample.

[0137] It should be noted that the acoustic and thermal performance warning level in the embodiment of the present invention can adopt a three-level warning mechanism, where the first level warning is a yellow warning and meets the following conditions: the acoustic and thermal performance warning probability is between 0.3 and 0.5; the second level warning is an orange warning and meets the following conditions: the acoustic and thermal performance warning probability is between 0.5 and 0.7; the third level warning is a red warning and meets the following conditions: the acoustic and thermal performance warning probability exceeds 0.7; accordingly, when judging whether the predicted current acoustic and thermal performance warning probability of the substation meets the warning standard, if it is determined that the predicted current acoustic and thermal performance warning probability of the substation is between 0.3 and 0.5, it is determined that the warning standard is met, and the corresponding first level warning (i.e., yellow warning) warning signal is triggered; if it is determined that the predicted current acoustic and thermal performance warning probability of the substation is between 0.5 and 0.7, it is determined that the warning standard is met, and the corresponding second level warning (i.e., orange warning) warning signal is triggered; if it is determined that the predicted current acoustic and thermal performance warning probability of the substation exceeds 0.7, it is determined that the warning standard is met, and the corresponding third level warning (i.e., red warning) warning signal is triggered.

[0138] It should be noted that the real-time monitoring status report of the substation can be set to be updated once every hour. It can include information such as trend charts of acoustic and thermal performance evaluation indicators, abnormal event statistics, and warning level distribution, so as to achieve real-time monitoring and early warning of the operating status of the composite layer structure.

[0139] An embodiment of the present invention provides a coordinated control method for noise reduction and fire prevention in a substation. By acquiring the original data of the substation operation and extracting the low-frequency noise characteristics, a network model of a composite layer structure of a bead board layer and an aluminum silicate fiber cotton layer is constructed, and the composite layer structure is optimized according to the principle of acoustic impedance matching, and the porosity of the bead board and the density of the aluminum silicate fiber cotton are adjusted to achieve effective attenuation of low-frequency noise. At the same time, the embodiment of the present invention also takes fire prevention performance into consideration. By analyzing the heat conduction path and adjusting the composite layer structure, it is ensured that the heat flux density meets the fire prevention requirements. Through acoustic simulation and parameter adjustment, a balance is sought between noise reduction and fire prevention performance to obtain the optimal physical parameters of the material. This scheme realizes the coordinated optimization of low-frequency noise control and fire prevention performance control of the substation, so that the noise reduction effect and fire prevention effect reach an ideal state at the same time, thereby meeting the dual needs of the substation for low-frequency noise control and high-temperature protection, and improving the safety and environmental friendliness of the substation operation.

[0140] The embodiment of the present invention further provides a noise reduction and fire prevention coordinated control system for a substation, which is used to implement the noise reduction and fire prevention coordinated control method for a substation described in any of the above embodiments, see Figure 2 FIG. 1 is a block diagram of a noise reduction and fire prevention coordinated control system for a substation according to an embodiment of the present invention. The system includes:

[0141] The noise frequency and amplitude distribution acquisition module 11 is used to obtain the original data of the substation and obtain the frequency and amplitude distribution of the target low-frequency noise based on the original data. The original data includes equipment operation load data and sound pressure level time series data.

[0142] an acoustic impedance mutation degree determining module 12, configured to determine the acoustic impedance mutation degree between two adjacent layers of a substation original composite board according to the frequency and amplitude distribution, the original composite board being composed of a floating bead board layer and an aluminum silicate fiber cotton layer;

[0143] A first physical parameter optimization module 13 is configured to optimize the first physical parameter of the original composite plate to obtain a first composite plate when the degree of the acoustic impedance mutation exceeds a preset mutation threshold;

[0144] a second physical parameter optimization module 14, configured to obtain a heat flux density of the first composite panel, and when the heat flux density exceeds a preset fire protection threshold, optimize a second physical parameter of the first composite panel to obtain a second composite panel;

[0145] a third physical parameter optimization module 15, configured to obtain a noise penetration index of the target low-frequency noise in the second composite panel, and, when the noise penetration index does not exceed a preset penetration threshold, optimize a third physical parameter of the second composite panel to obtain a third composite panel;

[0146] The acoustic-thermal performance balance control module 16 is configured to obtain the acoustic impedance distribution and the thermal conductivity distribution of the third composite plate, and determine the final physical parameters of the third composite plate corresponding to the acoustic-thermal performance balance point based on the acoustic impedance distribution and the thermal conductivity distribution, so as to adjust the structure of the third composite plate.

[0147] Preferably, the noise frequency and amplitude distribution acquisition module 11 specifically includes:

[0148] A data denoising and standardization processing unit, configured to perform denoising and standardization processing on the raw data to obtain standardized equipment operation load data and standardized sound pressure level time series data;

[0149] a data filtering processing unit, configured to segment the standardized sound pressure level time series data according to the standardized equipment operation load data, and perform band-pass filtering on the segmented sound pressure level time series data according to a preset target frequency band to obtain a target low-frequency band signal;

[0150] a time-frequency distribution acquisition unit, configured to sequentially perform multi-scale wavelet decomposition and Hilbert transform on the target low-frequency band signal to obtain the time-frequency distribution of the target low-frequency band signal;

[0151] The noise frequency and amplitude distribution acquisition unit is used to extract the frequency components whose amplitude exceeds the preset noise reference value from the time-frequency distribution, and perform spectrum analysis using fast Fourier transform to obtain the frequency and amplitude distribution of the target low-frequency band noise.

[0152] Preferably, the acoustic impedance mutation degree determining module 12 specifically includes:

[0153] a three-dimensional structure construction unit, configured to construct a three-dimensional structure of the original composite plate according to the frequency and amplitude distribution and in combination with original physical parameters of the original composite plate of the substation;

[0154] an acoustic impedance change rate calculation unit, configured to calculate a relative change rate of the acoustic impedance between the floating bead layer and the aluminum silicate fiber cotton layer according to the interface acoustic impedance of the floating bead layer and the aluminum silicate fiber cotton layer in the three-dimensional structure;

[0155] The acoustic impedance mutation degree determining unit is used to determine the acoustic impedance mutation degree between the floating bead plate layer and the aluminum silicate fiber cotton layer according to the relative change rate.

[0156] Preferably, the first physical parameter includes the porosity of the floating bead board layer and the density of the aluminum silicate fiber cotton layer;

[0157] Then, the first physical parameter optimization module 13 specifically includes:

[0158] A first parameter set acquisition unit is configured to acquire a first parameter set according to a preset adjustment range and adjustment amplitude of the porosity of the floating bead layer and a preset adjustment range and adjustment amplitude of the density of the aluminum silicate fiber cotton layer;

[0159] A second parameter set acquisition unit is configured to select from the first parameter set a plurality of parameter combinations that satisfy a lower limit value of the compressive strength of the floating bead board layer and an upper limit value of the elastic modulus of the aluminum silicate fiber cotton layer, to obtain a second parameter set;

[0160] A third parameter set acquisition unit is configured to select from the second parameter set several parameter combinations that satisfy the total thickness range and interlayer bonding area ratio of the original composite plate to obtain a third parameter set;

[0161] an acoustic impedance matching calculation unit, configured to calculate, for each parameter combination in the third parameter set, a ratio of the corresponding adjacent interface acoustic impedances as the acoustic impedance matching;

[0162] a first physical parameter iteration unit, configured to iteratively optimize the parameter combination in the third parameter set according to the acoustic impedance matching degree to obtain a final parameter combination, wherein during the iterative optimization process, the adjustment range of the porosity of the floating bead plate layer and the adjustment range of the density of the aluminum silicate fiber cotton layer decrease as the number of iterations increases;

[0163] The first physical parameter optimization unit is used to optimize the porosity of the floating bead board layer and the density of the aluminum silicate fiber cotton layer according to the final parameter combination to obtain a first composite board.

[0164] Preferably, the second physical parameter optimization module 14 specifically includes:

[0165] A three-dimensional grid unit establishment unit, used to establish a three-dimensional grid unit of the first composite board, and add the thermal conductivity, density and specific heat capacity of the floating bead board layer and the aluminum silicate fiber cotton layer to a grid unit attribute table;

[0166] The heat conduction equation establishment unit is used to establish the heat conduction equation in each grid cell using the external heat source temperature and the ambient temperature as the first-class boundary conditions and the convection heat transfer coefficient as the third-class boundary condition;

[0167] A temperature field distribution acquisition unit is used to discretize and solve the heat conduction equation using a finite volume method to obtain a temperature field distribution;

[0168] A temperature gradient acquisition unit, configured to obtain a temperature gradient using a central difference method according to the temperature field distribution;

[0169] The heat flux density acquisition unit is used to identify the heat conduction path according to the temperature gradient using a heat streamline tracing algorithm and acquire the heat flux density on the heat conduction path.

[0170] Preferably, the second physical parameter includes the porosity of the floating bead board layer and the thickness of the aluminum silicate fiber cotton layer;

[0171] Then, the second physical parameter optimization module 14 further includes:

[0172] an adjustment amount acquisition unit, configured to mark an area on the heat conduction path where the heat flux density exceeds a preset fire protection threshold, and to acquire a porosity adjustment amount of the floating bead board layer and a thickness adjustment amount of the aluminum silicate fiber cotton layer corresponding to the area;

[0173] a three-dimensional grid unit updating unit, configured to update the three-dimensional grid units of the first composite plate according to the porosity adjustment amount and the thickness adjustment amount, wherein the grid size of the updated three-dimensional grid units is determined by the minimum characteristic size of the floating bead plate layer and the aluminum silicate fiber cotton layer;

[0174] A heat conduction path reconstruction unit is used to identify a new heat conduction path based on the updated three-dimensional grid unit and obtain a new heat flux density on the new heat conduction path;

[0175] The second physical parameter optimization unit is used to determine whether the new heat flux density exceeds the preset fire protection threshold. If so, the second physical parameter of the first composite board is re-optimized until the new heat flux density obtained does not exceed the preset fire protection threshold. If not, the porosity of the floating bead board layer and the thickness of the aluminum silicate fiber cotton layer are optimized according to the currently obtained porosity adjustment amount and thickness adjustment amount to obtain the second composite board.

[0176] Preferably, the third physical parameter optimization module 15 specifically includes:

[0177] an acoustic grid unit division unit, configured to divide the acoustic grid unit according to the structure of the second composite plate using a quarter-wavelength criterion, and to add acoustic parameters of the bead plate layer and the aluminum silicate fiber cotton layer, wherein the acoustic parameters include density distribution, sound velocity distribution, and acoustic impedance distribution;

[0178] A propagation path simulation unit is used to set the incident angle range and frequency range of the sound wave, and for each combination of incident angle and frequency, use a sound wave tracking algorithm to simulate the propagation path of the target low-frequency noise in the acoustic grid unit;

[0179] an attenuation calculation unit, configured to calculate the attenuation of the target low-frequency noise during propagation according to a material sound absorption coefficient, wherein the material sound absorption coefficient is determined by the density distribution and sound velocity distribution of the floating bead board layer and the aluminum silicate fiber cotton layer;

[0180] a reflection and transmission coefficient calculation unit, configured to calculate the reflection coefficient and transmission coefficient of the target low-frequency noise at the interface using a transfer matrix method according to the acoustic impedance distribution of the floating bead board layer and the aluminum silicate fiber cotton layer;

[0181] a time-domain sound pressure signal acquiring unit, configured to acquire a time-domain sound pressure signal on the propagation path according to the attenuation, the reflection coefficient, and the transmission coefficient;

[0182] a frequency domain sound pressure spectrum acquisition unit, configured to perform a fast Fourier transform on the time domain sound pressure signal to obtain a frequency domain sound pressure spectrum;

[0183] Noise penetration index calculation unit, used to calculate the noise penetration index according to the formula N P =|Pout| / |Pin| Calculate the noise penetration index of the target low-frequency noise in the second composite board, N Prepresents the noise penetration index, Pin and Pout represent the input sound pressure of one surface and the output sound pressure of the other surface of the second composite plate respectively, and Pin and Pout are obtained according to the frequency domain sound pressure spectrum.

[0184] Preferably, the third physical parameter includes the porosity and pore size of the floating bead layer and the density of the aluminum silicate fiber cotton layer;

[0185] Then, the third physical parameter optimization module 15 further includes:

[0186] A third physical parameter set acquisition unit is configured to obtain a third physical parameter set based on an adjustment range and an adjustment amplitude of the porosity of the floating bead layer, an adjustment range and an adjustment amplitude of the pore size, and an adjustment range and an adjustment amplitude of the density of the aluminum silicate fiber cotton layer;

[0187] A parameter influence weight calculation unit is used to calculate the influence weights of the porosity and pore size of the floating bead plate layer and the density of the aluminum silicate fiber cotton layer on the acoustic impedance using a sensitivity analysis or partial derivative method;

[0188] a parameter hierarchical screening unit, configured to perform hierarchical screening on each third physical parameter in the third physical parameter set according to the influence weight and in accordance with an acoustic impedance matching principle, and establish a response relationship between the acoustic impedance and each third physical parameter;

[0189] a third physical parameter iteration unit, configured to obtain an optimal parameter combination of each third physical parameter in the third physical parameter set by adopting an iterative optimization algorithm;

[0190] The third physical parameter optimization unit is used to optimize the porosity and pore size of the floating bead board layer and the density of the aluminum silicate fiber cotton layer according to the optimal parameter combination to obtain a third composite board.

[0191] Preferably, the acoustic-thermal performance balance control module 16 specifically includes:

[0192] an acoustic impedance distribution acquiring unit, configured to establish an acoustic grid unit of the third composite plate, and calculate the acoustic impedance of each grid unit to obtain an acoustic impedance distribution of the acoustic grid unit;

[0193] a thermal conductivity distribution acquisition unit, configured to acquire the thermal conductivity distribution of the acoustic grid unit through heat conduction simulation or experimental measurement according to the acoustic impedance distribution;

[0194] an acoustic-thermal performance coupling matrix establishing unit, configured to establish an acoustic-thermal performance coupling matrix according to the acoustic impedance distribution and the thermal conductivity distribution;

[0195] The acoustic thermal performance function establishment unit is used to set the normalized noise reduction index and fire prevention index, and establish the acoustic thermal performance function according to the noise reduction index and the fire prevention index. The acoustic thermal performance function is f(N, F)=w N ×(1-N)+w F ×F, N represents the noise reduction index, w N represents the weight of the noise reduction index, F represents the fire prevention index, w F Indicates the weight of the fire protection index;

[0196] an acoustic-thermal performance balance point determination unit, configured to determine, within a preset weight value range, an optimal weight combination of the noise reduction index and the fire protection index in the acoustic-thermal performance function using an iterative optimization algorithm to determine an acoustic-thermal performance balance point; and wherein the acoustic-thermal performance coupling matrix is ​​configured to evaluate, during the iterative optimization process, the comprehensive effects of adjustments to the porosity and pore size of the floating bead layer and the density of the aluminum silicate fiber cotton layer on the acoustic-thermal performance;

[0197] The composite plate parameter adjustment unit is used to obtain the porosity, pore size and density of the aluminum silicate fiber cotton layer of the floating bead plate layer corresponding to the acoustic and thermal performance balance point, so as to adjust the structure of the third composite plate according to the porosity, pore size and density of the aluminum silicate fiber cotton layer of the floating bead plate layer corresponding to the acoustic and thermal performance balance point.

[0198] It should be noted that the coordinated control system for noise reduction and fire prevention of a substation provided by an embodiment of the present invention can realize all the processes of the coordinated control method for noise reduction and fire prevention of a substation described in any of the above embodiments. The functions of each module and unit in the system and the technical effects achieved are respectively the same as the functions and technical effects achieved by the coordinated control method for noise reduction and fire prevention of a substation described in the above embodiments, and will not be repeated here.

[0199] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A coordinated control method for noise reduction and fire prevention in a substation, characterized in that: include: Obtaining raw data of the substation, and obtaining the frequency and amplitude distribution of target low-frequency noise based on the raw data, wherein the raw data includes equipment operation load data and sound pressure level time series data; Determining the degree of acoustic impedance mutation between two adjacent layers of media of an original composite plate of a substation according to the frequency and amplitude distribution, wherein the original composite plate is composed of a floating bead plate layer and an aluminum silicate fiber cotton layer; When the degree of the acoustic impedance mutation exceeds a preset mutation threshold, optimizing the first physical parameters of the original composite board to obtain a first composite board, wherein the first physical parameters include the porosity of the floating bead board layer and the density of the aluminum silicate fiber cotton layer; Obtaining a heat flux density of the first composite panel, and when the heat flux density exceeds a preset fire protection threshold, optimizing a second physical parameter of the first composite panel to obtain a second composite panel, wherein the second physical parameter includes a porosity of the floating bead layer and a thickness of the aluminum silicate fiber cotton layer; Obtaining a noise penetration index of the target low-frequency noise in the second composite panel, and when the noise penetration index does not exceed a preset penetration threshold, optimizing third physical parameters of the second composite panel to obtain a third composite panel, wherein the third physical parameters include the porosity and pore size of the floating bead layer and the density of the aluminum silicate fiber cotton layer; The acoustic impedance distribution and thermal conductivity distribution of the third composite plate are obtained, and the final physical parameters of the third composite plate corresponding to the acoustic-thermal performance balance point are determined based on the acoustic impedance distribution and the thermal conductivity distribution to adjust the structure of the third composite plate. The final physical parameters include the porosity and pore size of the floating bead layer and the density of the aluminum silicate fiber cotton layer.

2. The coordinated control method for noise reduction and fire prevention in a substation according to claim 1, characterized in that: The obtaining of the frequency and amplitude distribution of the target low-frequency noise according to the original data specifically includes: De-noising and standardizing the raw data to obtain standardized equipment operation load data and standardized sound pressure level time series data; Segmenting the standardized sound pressure level time series data according to the standardized equipment operation load data, and performing band-pass filtering on the segmented sound pressure level time series data according to a preset target frequency band to obtain a target low-frequency band signal; performing multi-scale wavelet decomposition and Hilbert transform on the target low-frequency band signal in sequence to obtain a time-frequency distribution of the target low-frequency band signal; Frequency components whose amplitudes exceed a preset noise reference value are extracted from the time-frequency distribution, and spectrum analysis is performed using fast Fourier transform to obtain the frequency and amplitude distribution of the target low-frequency band noise.

3. The coordinated control method for noise reduction and fire prevention in a substation according to claim 1, characterized in that: Determining the degree of acoustic impedance mutation between two adjacent layers of media of the original composite plate of the substation according to the frequency and amplitude distribution specifically includes: According to the frequency and amplitude distribution, combined with the original physical parameters of the original composite plate of the substation, constructing the three-dimensional structure of the original composite plate; Calculating a relative change rate of the acoustic impedance between the floating bead layer and the aluminum silicate fiber cotton layer according to the interface acoustic impedance between the floating bead layer and the aluminum silicate fiber cotton layer in the three-dimensional structure; According to the relative change rate, the degree of acoustic impedance mutation between the floating bead plate layer and the aluminum silicate fiber cotton layer is determined.

4. The coordinated control method for noise reduction and fire prevention in a substation according to claim 1, characterized in that: Optimizing the first physical parameter of the original composite plate to obtain the first composite plate specifically includes: Obtaining a first parameter set according to the preset adjustment range and adjustment amplitude of the porosity of the floating bead layer and the adjustment range and adjustment amplitude of the density of the aluminum silicate fiber cotton layer; Selecting from the first parameter set several parameter combinations that satisfy the lower limit of the compressive strength of the floating bead board layer and the upper limit of the elastic modulus of the aluminum silicate fiber cotton layer to obtain a second parameter set; Selecting from the second parameter set several groups of parameter combinations that satisfy the total thickness range and interlayer bonding area ratio of the original composite plate to obtain a third parameter set; For each parameter combination in the third parameter set, calculating the ratio of the corresponding adjacent interface acoustic impedances as the acoustic impedance matching degree; Iteratively optimizing the parameter combination in the third parameter set according to the acoustic impedance matching to obtain a final parameter combination, wherein during the iterative optimization process, the adjustment range of the porosity of the floating bead layer and the adjustment range of the density of the aluminum silicate fiber cotton layer decrease as the number of iterations increases; The porosity of the floating bead board layer and the density of the aluminum silicate fiber cotton layer are optimized according to the final parameter combination to obtain a first composite board.

5. The coordinated control method for noise reduction and fire prevention in a substation according to claim 1, characterized in that: The obtaining of the heat flux density of the first composite plate specifically includes: Establishing a three-dimensional grid unit of the first composite board, and adding the thermal conductivity, density, and specific heat capacity of the floating bead board layer and the aluminum silicate fiber cotton layer to a grid unit attribute table; The external heat source temperature and the ambient temperature are used as the first-type boundary conditions, and the convection heat transfer coefficient is used as the third-type boundary condition. The heat conduction equation is established in each grid cell. The heat conduction equation is discretized and solved using the finite volume method to obtain the temperature field distribution; According to the temperature field distribution, a temperature gradient is obtained using a central difference method; According to the temperature gradient, a heat flow line tracing algorithm is used to identify a heat conduction path, and a heat flux density on the heat conduction path is obtained.

6. The coordinated control method for noise reduction and fire prevention in a substation according to claim 5, characterized in that: Optimizing the second physical parameter of the first composite plate to obtain the second composite plate specifically includes: Marking areas on the heat conduction path where the heat flux density exceeds a preset fire protection threshold, and obtaining porosity adjustment amounts of the floating bead board layer and thickness adjustment amounts of the aluminum silicate fiber cotton layer corresponding to the areas; updating the three-dimensional grid unit of the first composite plate according to the porosity adjustment amount and the thickness adjustment amount, wherein the grid size of the updated three-dimensional grid unit is determined by the minimum characteristic size of the floating bead plate layer and the aluminum silicate fiber cotton layer; Identify a new heat conduction path based on the updated three-dimensional grid unit and obtain a new heat flux density on the new heat conduction path; Determine whether the new heat flux density exceeds the preset fire protection threshold. If so, re-optimize the second physical parameter of the first composite board until the new heat flux density obtained does not exceed the preset fire protection threshold. If not, optimize the porosity of the floating bead board layer and the thickness of the aluminum silicate fiber cotton layer according to the currently obtained porosity adjustment amount and thickness adjustment amount to obtain a second composite board.

7. The coordinated control method for noise reduction and fire prevention in a substation according to claim 1, characterized in that: The obtaining of the noise penetration index of the target low-frequency noise in the second composite plate specifically includes: According to the structure of the second composite plate, the acoustic grid unit is divided using the quarter-wavelength criterion, and the acoustic parameters of the floating bead plate layer and the aluminum silicate fiber cotton layer are added, wherein the acoustic parameters include density distribution, sound velocity distribution, and acoustic impedance distribution; Setting the incident angle range and frequency range of the sound wave, and for each combination of incident angle and frequency, using a sound wave tracking algorithm to simulate the propagation path of the target low-frequency noise in the acoustic grid unit; Calculating the attenuation of the target low-frequency noise during propagation based on the material sound absorption coefficient, wherein the material sound absorption coefficient is determined by the density distribution and sound velocity distribution of the floating bead board layer and the aluminum silicate fiber cotton layer; According to the acoustic impedance distribution of the floating bead board layer and the aluminum silicate fiber cotton layer, the reflection coefficient and the transmission coefficient of the target low-frequency noise at the interface are calculated using the transfer matrix method; Acquiring a time-domain sound pressure signal on the propagation path according to the attenuation, the reflection coefficient, and the transmission coefficient; Performing a fast Fourier transform on the time-domain sound pressure signal to obtain a frequency-domain sound pressure spectrum; According to the formula N P =|Pout| / |Pin| Calculate the noise penetration index of the target low-frequency noise in the second composite board, N P represents the noise penetration index, Pin and Pout represent the input sound pressure of one surface and the output sound pressure of the other surface of the second composite plate respectively, and Pin and Pout are obtained according to the frequency domain sound pressure spectrum.

8. The coordinated control method for noise reduction and fire prevention in a substation according to claim 1, characterized in that: Optimizing the third physical parameter of the second composite plate to obtain the third composite plate specifically includes: Obtaining a third set of physical parameters according to the adjustment range and adjustment amplitude of the porosity of the floating bead layer, the adjustment range and adjustment amplitude of the pore size, and the adjustment range and adjustment amplitude of the density of the aluminum silicate fiber cotton layer; Using sensitivity analysis or partial derivative method, calculate the influence weight of the porosity and pore size of the floating bead layer and the density of the aluminum silicate fiber cotton layer on the acoustic impedance; According to the influence weight and in accordance with the acoustic impedance matching principle, the third physical parameters in the third physical parameter set are graded and screened to establish a response relationship between the acoustic impedance and the third physical parameters; Using an iterative optimization algorithm to obtain an optimal parameter combination of each third physical parameter in the third physical parameter set; The porosity and pore size of the floating bead board layer and the density of the aluminum silicate fiber cotton layer are optimized according to the optimal parameter combination to obtain a third composite board.

9. The coordinated control method for noise reduction and fire prevention in a substation according to claim 1, characterized in that: Obtaining the acoustic impedance distribution and thermal conductivity distribution of the third composite plate, and determining final physical parameters of the third composite plate corresponding to an acoustic-thermal performance balance point according to the acoustic impedance distribution and the thermal conductivity distribution to adjust the structure of the third composite plate, specifically includes: Establishing an acoustic grid unit of the third composite plate, and calculating the acoustic impedance of each grid unit to obtain an acoustic impedance distribution of the acoustic grid unit; According to the acoustic impedance distribution, obtaining the thermal conductivity distribution of the acoustic grid unit through heat conduction simulation or experimental measurement; Establishing an acoustic-thermal performance coupling matrix according to the acoustic impedance distribution and the thermal conductivity distribution; Set a normalized noise reduction index and a fire prevention index, and establish an acoustic thermal performance function based on the noise reduction index and the fire prevention index. The acoustic thermal performance function is f(N, F)=w N ×(1-N)+w F ×F, N represents the noise reduction index, w N represents the weight of the noise reduction index, F represents the fire prevention index, w F Indicates the weight of the fire protection index; Within a preset weight value range, an iterative optimization algorithm is used to determine the optimal weight combination of the noise reduction index and the fire protection index in the acoustic and thermal performance function to determine the acoustic and thermal performance balance point. The acoustic and thermal performance coupling matrix is ​​used to evaluate the comprehensive impact of the adjustment of the porosity and pore size of the floating bead board layer and the density of the aluminum silicate fiber cotton layer on the acoustic and thermal performance during the iterative optimization process; The porosity, pore size and density of the aluminum silicate fiber cotton layer of the floating bead board layer corresponding to the acoustic and thermal performance balance point are obtained, so as to adjust the structure of the third composite board according to the porosity, pore size and density of the aluminum silicate fiber cotton layer of the floating bead board layer corresponding to the acoustic and thermal performance balance point.

10. A coordinated control system for noise reduction and fire prevention in a substation, characterized in that: include: A noise frequency and amplitude distribution acquisition module is used to acquire raw data from the substation and obtain the frequency and amplitude distribution of the target low-frequency noise based on the raw data, wherein the raw data includes equipment operation load data and sound pressure level time series data; an acoustic impedance mutation degree determination module, configured to determine the acoustic impedance mutation degree between two adjacent layers of a substation's original composite board based on the frequency and amplitude distribution, the original composite board comprising a bead board layer and an aluminum silicate fiber cotton layer; a first physical parameter optimization module, configured to optimize first physical parameters of the original composite board to obtain a first composite board when the degree of the acoustic impedance mutation exceeds a preset mutation threshold, wherein the first physical parameters include the porosity of the floating bead board layer and the density of the aluminum silicate fiber cotton layer; a second physical parameter optimization module, configured to obtain a heat flux density of the first composite panel and, when the heat flux density exceeds a preset fire protection threshold, optimize a second physical parameter of the first composite panel to obtain a second composite panel, wherein the second physical parameter includes a porosity of the floating bead panel layer and a thickness of the aluminum silicate fiber cotton layer; a third physical parameter optimization module, configured to obtain a noise penetration index of the target low-frequency noise in the second composite panel, and, when the noise penetration index does not exceed a preset penetration threshold, optimize third physical parameters of the second composite panel to obtain a third composite panel, wherein the third physical parameters include the porosity and pore size of the floating bead layer and the density of the aluminum silicate fiber cotton layer; The acoustic-thermal performance balance control module is used to obtain the acoustic impedance distribution and thermal conductivity distribution of the third composite plate, and determine the final physical parameters of the third composite plate corresponding to the acoustic-thermal performance balance point based on the acoustic impedance distribution and the thermal conductivity distribution to adjust the structure of the third composite plate. The final physical parameters include the porosity and pore size of the floating bead layer and the density of the aluminum silicate fiber cotton layer.

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