Method and system for evaluating acoustic performance of SPC floor anti-noise structure

Abstract

The invention relates to the technical field of building acoustic performance evaluation, in particular to an SPC floor anti-noise structure acoustic performance evaluation method and system, and the method comprises the steps: collecting the acoustic structure attributes of an SPC floor, and setting a plurality of structure acoustic evaluation factors according to the acoustic performance evaluation requirements of the SPC floor, the method comprises the following steps of: respectively allocating acoustic response weights to a plurality of structural acoustic evaluation factors according to acoustic structure attributes, respectively allocating combined secondary weights to the plurality of structural acoustic evaluation factors according to the acoustic structure attributes, finally constructing an acoustic evaluation model, and outputting an evaluation result by combining the acoustic response weights and the combined secondary weights. The problem that traditional acoustic evaluation lacks a dynamic adjustment evaluation weight mechanism is effectively solved, the continuity of material and structure change response is enhanced, the cost is reduced, and the acoustic effect is accurately predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of building acoustic performance evaluation, and in particular to an acoustic performance evaluation method and system for a noise-proof structure of a SPC floor. Background Art

[0002] In the building materials and interior design industries, SPC (Stone Plastic Composite) flooring is popular for its excellent noise-proof performance and durability. With the improvement of environmental awareness and the increase in consumer demand for a comfortable living environment, efficient acoustic performance evaluation methods have become the key to the development of the industry. SPC flooring manufacturers and designers are constantly seeking more advanced evaluation tools and methods to ensure that new products can meet strict consumer standards in a highly competitive market.

[0003] However, although existing technologies can provide basic acoustic performance analysis, there are still deficiencies in how to systematically integrate historical data, evaluate weights and implement continuous optimization. Traditional methods often rely on data collection at a single point in time, ignoring the value of historical performance data in continuous improvement. In addition, the lack of a mechanism for dynamically adjusting evaluation weights means that acoustic performance evaluation often fails to reflect the actual changes in material and structural properties over time, thereby limiting the accuracy and practicality of the evaluation results.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art known to those skilled in the art. Summary of the invention

[0005] The present invention provides a method and system for evaluating the acoustic performance of an SPC floor noise-proof structure, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: A method for evaluating the acoustic performance of a noise-proof structure of an SPC floor, the method comprising: Acoustic structural properties of the SPC floor are collected, and several structural acoustic evaluation factors are established according to the acoustic performance evaluation requirements of the SPC floor, wherein the acoustic structural properties include noise-proof material properties and noise-proof structural properties; Allocating acoustic response weights to the plurality of structural acoustic assessment factors respectively, and allocating combined secondary weights to the plurality of structural acoustic assessment factors respectively according to the acoustic construction properties; An acoustic evaluation model is constructed, and a structural acoustic performance evaluation result is output according to the acoustic evaluation model in combination with the acoustic response weight and the combined secondary weight.

[0007] Furthermore, acoustic response weights are respectively assigned to the structural acoustic assessment factors, including: Collecting historical attribute parameters of the SPC floor, allocating initial evaluation weights according to the historical attribute parameters of the SPC floor and a number of the structural acoustic evaluation factors, and calculating the initial total score according to the initial evaluation weights; Based on the initial evaluation weight, mark any of the structural acoustic evaluation factors as a variable factor, and mark the other structural acoustic evaluation factors as constant factors, respectively increase or decrease the initial evaluation weight of the variable factor, and calculate a total score, compare the total score with the initial total score, and obtain a change in the total score; Cancel the mark of the variable factor, select any of the constant factors to be marked as the variable factor, use the canceled variable factor as the constant factor again, increase or decrease the initial evaluation weight of the variable factor respectively, calculate the total score, and compare it with the initial total score to obtain the change in the total score; Repeating marking the variable factors until all the structural acoustics assessment factors serve as the variable factors, and obtaining the total score change corresponding to the variable factors; According to the total score change, as the influence of the plurality of structural acoustics assessment factors on the structural acoustics performance assessment, acoustic response weights are allocated according to the influence.

[0008] Further, calculating the initial total score according to the initial evaluation weight includes: S1: collecting historical acoustic test parameters, performing performance analysis on the historical acoustic test parameters according to the control variable method, obtaining performance analysis results, assigning scores to several structural acoustic evaluation factors based on the performance analysis results, and obtaining individual scores corresponding to several structural acoustic evaluation factors; S2: integrating the individual scores corresponding to the plurality of structural acoustic assessment factors with the initial assessment weights respectively to obtain the individual contributions corresponding to the structural acoustic assessment factors, and combining the individual contributions corresponding to the plurality of structural acoustic assessment factors to obtain an initial total score; The total score change is obtained by calculating the total score according to steps S1 and S2, and calculating the difference between the total score and the initial total score to obtain the total score change.

[0009] Furthermore, according to the acoustic construction properties, a combined secondary weight is assigned to a plurality of the structural acoustic assessment factors, including: Establishing a number of secondary acoustic evaluation factors according to the properties of the noise-proof material and the noise-proof structure, wherein the secondary acoustic evaluation factors include the ratio of natural stone powder to polyvinyl chloride, and the proportion of the core layer, the wear-resistant layer, and the bottom layer; The acoustic influence standard is set by using a scaling method, and the ratio of the natural stone powder to the polyvinyl chloride and the proportion of the core layer, the wear-resistant layer and the bottom layer are evaluated according to the acoustic influence standard to obtain an evaluation value, wherein the evaluation is to evaluate the material properties of the natural stone powder and the polyvinyl chloride and the structural properties of the proportion of the core layer, the wear-resistant layer and the bottom layer respectively; constructing secondary factor evaluation matrices according to the evaluation values, respectively, wherein the rows and columns of the secondary factor evaluation matrix are the secondary acoustic evaluation factors, and the element values ​​in the matrix are the evaluation values; Calculating the maximum eigenvalue of the secondary factor evaluation matrix by a mathematical method, obtaining a corresponding eigenvector according to the maximum eigenvalue, normalizing the eigenvector to obtain a normalized eigenvector, wherein several element values ​​in the normalized eigenvector represent secondary evaluation weights of the secondary acoustic evaluation factor; combining the secondary evaluation weight of the noise-proof material property with the secondary evaluation weight of the noise-proof structure property to obtain a combined secondary weight; Furthermore, an acoustic evaluation model is constructed, including: Constructing a weight verification database, wherein the weight verification database includes the historical attribute parameters of the SPC floor and the historical acoustic test parameters; Extracting a mapping relationship between the historical attribute parameters of the SPC floor and the historical acoustic test parameters according to the weight verification database, and obtaining a number of historical acoustic response weights and historical combined secondary weights according to the mapping relationship; According to the historical acoustic response weight and the historical combined secondary weight, a deviation analysis is performed on the acoustic response weight and the combined secondary weight, and according to the deviation analysis result, the acoustic response weight and the combined secondary weight are optimized to obtain an optimization result, and an acoustic evaluation model is constructed according to the optimization result.

[0010] Furthermore, a weight verification database is constructed, including: Extracting historical material attribute information and historical structural attribute information according to the historical attribute parameters of the SPC floor to obtain an acoustic evaluation feature set; Using a machine learning algorithm to perform deep learning on the acoustic evaluation feature set to obtain a deep learning result; Determine the standard material ratio and the standard structure proportion according to the deep learning result, and allocate the secondary weight of the historical combination according to the standard material ratio and the standard structure proportion; Performing secondary learning on the historical combined secondary weights and the historical acoustic test parameters to obtain historical acoustic response weights, and constructing a mapping relationship based on the historical combined secondary weights and the historical acoustic response weights; The mapping relationship and historical acoustic test parameters are combined and indexed to construct a weight verification database.

[0011] Further, performing deviation analysis on the acoustic response weight and the combined secondary weight according to the historical acoustic response weight and the historical combined secondary weight, and optimizing the acoustic response weight and the combined secondary weight according to the deviation analysis result, including: The historical acoustic response weight and the historical combined secondary weight are respectively used as standard values, and the acoustic response weight and the combined secondary weight are respectively used as actual values, and the deviation analysis is performed respectively using the loss function to obtain the deviation analysis result; Identify the main deviation factors according to the deviation results, re-evaluate the weights of the main deviation factors, and use the loss function again to perform deviation analysis according to the re-evaluation results to obtain secondary deviation analysis results; Iterative optimization is performed according to the secondary deviation analysis result to obtain an optimization result.

[0012] Furthermore, the deviation analysis is performed using the loss function, including: ; in, represents the actual value, Indicates the standard value, is the number of acoustic response weights or combined secondary weights, The deviation analysis result is shown.

[0013] SPC floor noise-proof structural acoustic performance evaluation system, the system includes: An evaluation factor setting module collects the acoustic structural properties of the SPC floor and sets up a number of structural acoustic evaluation factors according to the acoustic performance evaluation requirements of the SPC floor, wherein the acoustic structural properties include noise-proof material properties and noise-proof structural properties; A multi-weight assignment module is configured to assign acoustic response weights to the plurality of structural acoustic assessment factors respectively, and to assign combined secondary weights to the plurality of structural acoustic assessment factors respectively according to the acoustic construction properties; The model building and output module builds an acoustic evaluation model, and outputs a structural acoustic performance evaluation result according to the acoustic evaluation model in combination with the acoustic response weight and the combined secondary weight.

[0014] Furthermore, the model building and output module includes: A weight verification database construction unit is used to construct a weight verification database, wherein the weight verification database includes the historical attribute parameters of the SPC floor and the historical acoustic test parameters; A historical weight data extraction unit extracts a mapping relationship between the historical attribute parameters of the SPC floor and the historical acoustic test parameters according to the weight verification database, and obtains a number of historical acoustic response weights and historical combined secondary weights according to the mapping relationship; The weight deviation analysis and optimization unit performs deviation analysis on the acoustic response weight and the combined secondary weight according to the historical acoustic response weight and the historical combined secondary weight, optimizes the acoustic response weight and the combined secondary weight according to the deviation analysis result, obtains the optimization result, and constructs the acoustic evaluation model according to the optimization result.

[0015] The technical solution of the present invention can achieve the following technical effects: This method effectively solves the problem of lack of dynamic adjustment mechanism of evaluation weights in traditional acoustic evaluation, thereby significantly improving the accuracy and practicality of acoustic performance evaluation. By utilizing historical performance data, this method not only enhances the continuity of response to material and structural changes, but also can adjust the evaluation model in real time to ensure that the evaluation results can reflect the latest situation in actual application. This efficient evaluation process not only shortens the product development cycle and reduces costs, but also accurately predicts acoustic effects.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is a flow chart of the method for evaluating the acoustic performance of the SPC floor noise-proof structure; Figure 2 Schematic diagram of the process of assigning acoustic response weights; Figure 3 Schematic diagram of the process for calculating the total score; Figure 4 Model structure diagram for acoustic evaluation; Figure 5 Assign structural diagrams for acoustic response weights. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0021] Embodiment 1; like Figure 1 As shown, the present application provides a method for evaluating the acoustic performance of a noise-proof structure of an SPC floor, the method comprising: S100: Collect the acoustic structural properties of the SPC floor and set up several structural acoustic evaluation factors according to the acoustic performance evaluation requirements of the SPC floor. The acoustic structural properties include the properties of the noise-proof material and the noise-proof structure. S200: assigning acoustic response weights to a plurality of structural acoustic assessment factors respectively, and assigning combined secondary weights to a plurality of structural acoustic assessment factors respectively according to acoustic construction properties; S300: construct an acoustic evaluation model, and output a structural acoustic performance evaluation result according to the acoustic evaluation model in combination with the acoustic response weight and the combined secondary weight.

[0022] Specifically, firstly, the acoustic structural properties of the SPC floor are comprehensively collected, including the properties of the noise-proof materials, such as the density, thickness and type of the materials, PVC, fillers, etc., and the noise-proof structural properties, such as the hierarchical structure, the combination of each layer of materials and their acoustic isolation characteristics. According to these performance parameters and the acoustic performance evaluation requirements of the SPC floor, such as sound absorption coefficient, warm-up performance, etc., several structural acoustic evaluation factors are established, and each structural acoustic evaluation factor is assigned an acoustic response weight. These weights can be based on empirical data obtained from similar applications and test results, as well as the advice of acoustic experts. In addition, combined secondary weights are assigned according to the interaction between the noise-proof materials and the structural properties, in order to consider the mutual influence between the material and structural properties, and to use the sound wave transmission as the basis. Taking the acoustic transmission loss as an example, considering its importance in residential floors, the sound wave transmission loss is assigned a higher acoustic response weight, and taking into account the noise-proof material properties of the high-density polyvinyl chloride (PVC) and stone powder mixture used, additional secondary weights are added to the evaluation factors directly related to the material density, and the weight distribution is further refined. Then, a comprehensive acoustic evaluation model is constructed using the assigned acoustic response weights and combined secondary weights. The model uses modern acoustic theories and calculation methods, such as finite element analysis and statistical energy analysis, to simulate the acoustic performance of SPC floors in actual applications and output acoustic performance evaluation results.

[0023] The technical solution of the present invention effectively solves the problem of lack of a dynamic adjustment mechanism for evaluation weights in traditional acoustic evaluations, thereby significantly improving the accuracy and practicality of acoustic performance evaluations. By utilizing historical performance data, this method not only enhances the continuity of responses to material and structural changes, but also can adjust the evaluation model in real time to ensure that the evaluation results can reflect the latest situation in actual applications. This efficient evaluation process not only shortens the product development cycle and reduces costs, but also accurately predicts acoustic effects.

[0024] Further, if Figure 2 and Figure 5 As shown in Figure 1, acoustic response weights are assigned to several structural acoustic assessment factors, including: S210: Collect historical attribute parameters of the SPC floor, assign initial evaluation weights according to the historical attribute parameters of the SPC floor and several structural acoustic evaluation factors, and calculate the initial total score according to the initial evaluation weights; S220: Based on the initial evaluation weight, mark any structural acoustic evaluation factor as a variable factor, and mark the other structural acoustic evaluation factors as constant factors, respectively increase or decrease the initial evaluation weight of the variable factor, and calculate the total score, compare the total score with the initial total score, and obtain the change in the total score; S230: cancel the mark of the variable factor, select any constant factor to be marked as the variable factor, use the canceled variable factor as the constant factor again, increase or decrease the initial evaluation weight of the variable factor respectively, calculate the total score, and compare it with the initial total score to obtain the change in the total score; S240: Repeat marking variable factors until all structural acoustics assessment factors serve as variable factors, and obtain the total score change of the corresponding variable factors; S250: Based on the change in the total score, as the influence of several structural acoustic assessment factors on the structural acoustic performance assessment, the acoustic response weight is allocated according to the influence.

[0025] As a preferred embodiment of the above, the historical attribute parameters of the SPC floor are first collected, including the actual performance data of the floor during use, such as the acoustic performance results of past tests (sound wave transmission loss and impact sound level, etc.), and the physical and chemical attribute records of the floor, such as material type, layer structure, density and thickness. These historical attribute parameters provide key baseline information for the subsequent evaluation steps. According to the correlation between the collected historical attribute parameters and the acoustic evaluation factors, an initial evaluation weight is assigned to each factor. These initial weights are based on the historical performance data and the expected performance requirements. The initial total score is calculated as the basis of the evaluation model, and then one by one the structural acoustic evaluation factors are selected as variable factors, that is, as variables, and the remaining factors are used as constant factors, that is, factors that keep the weight unchanged. Then, by adjusting The weight of the variable factor is increased or decreased to observe the change in the total score. After obtaining the change in the total score of a structural acoustic assessment factor, the previously marked variable factor is cancelled and a new constant factor is selected as the new variable factor for the same process. Each factor will be tested as a variable factor in turn to ensure a comprehensive evaluation of the impact of all factors. Based on the change in the total score shown by each factor in the test, the acoustic response weight is redistributed. Factors with large changes will receive higher weights, which reflects their importance in improving or affecting acoustic performance. For example, increasing or decreasing the sound wave transmission loss can significantly affect the total score, while an increase or decrease in the impact sound level can only have a smaller impact on the total score. This means that the actual weight of the sound wave transmission loss should be greater than the actual weight of the impact sound level and have a higher influence.

[0026] Furthermore, if Figure 3 As shown, the initial total score is calculated based on the initial evaluation weights, including: S1: Collect historical acoustic test parameters, perform performance analysis on the historical acoustic test parameters according to the control variable method, obtain performance analysis results, assign scores to several structural acoustic evaluation factors based on the performance analysis results, and obtain individual scores corresponding to several structural acoustic evaluation factors; S2: Integrate the individual scores corresponding to several structural acoustic assessment factors with the initial assessment weights to obtain the individual contributions of the corresponding structural acoustic assessment factors, and combine the individual contributions corresponding to several structural acoustic assessment factors to obtain the initial total score; The total score change is obtained by calculating the total score according to steps S1 and S2, and then calculating the difference between the total score and the initial total score to obtain the total score change.

[0027] In this embodiment, acoustic test data of SPC floor is first collected from past test records, and then control variables are used to determine variables that remain unchanged during the analysis period, including test temperature, humidity, test equipment, etc., to obtain a number of test data, and statistical analysis is performed on each set of test data, such as calculating statistical indicators such as mean value and variance for analysis, to obtain performance analysis results, and then the thresholds of excellent performance and substandard performance are determined by setting the performance benchmark of each acoustic test, and each test result is scored according to the performance analysis results. For example, a scoring system from 1 to 10 can be set, where 10 indicates that the performance is significantly higher than the benchmark, and 1 indicates that the performance is significantly lower than the benchmark. The score can be quantified according to the gap between the test result and the benchmark. For example, if the STL value exceeds the industry standard by more than 10%, a high score (such as 9 or 10 points) may be given; if it is less than 10%, a low score (such as 1 or 2 points) is given. The individual contribution is obtained according to the score of each structural acoustic evaluation factor and the initial evaluation weight, and the individual contribution of each factor is added to calculate the initial total score. Similarly, the change in the total score is obtained in the same way.

[0028] Furthermore, if Figure 4 As shown in Figure 1, several structural acoustic assessment factors are assigned combined secondary weights based on the acoustic construction properties, including: According to the properties of noise-proof materials and noise-proof structures, several secondary acoustic evaluation factors are established. The secondary acoustic evaluation factors include the ratio of natural stone powder to polyvinyl chloride, and the proportion of the core layer, wear-resistant layer and bottom layer. The acoustic influence standard is set by using the scaling method. The ratio of natural stone powder to polyvinyl chloride and the proportion of the core layer, the wear-resistant layer and the bottom layer are evaluated according to the acoustic influence standard to obtain an evaluation value, which is to evaluate the material properties of natural stone powder and polyvinyl chloride and the structural properties of the proportion of the core layer, the wear-resistant layer and the bottom layer respectively; According to the evaluation values, secondary factor evaluation matrices are constructed respectively, the rows and columns of the secondary factor evaluation matrix are secondary acoustic evaluation factors, and the element values ​​in the matrix are evaluation values; The maximum eigenvalue of the secondary factor evaluation matrix is ​​calculated by a mathematical method, and the corresponding eigenvector is obtained according to the maximum eigenvalue. The eigenvector is normalized to obtain a normalized eigenvector, and several element values ​​in the normalized eigenvector represent the secondary evaluation weights of the secondary acoustic evaluation factors; The secondary evaluation weight of the noise-proofing material property is combined with the secondary evaluation weight of the noise-proofing structure property to obtain a combined secondary weight.

[0029] Specifically, first, the secondary acoustic evaluation factors are clearly defined according to the material and structural characteristics of the SPC floor. For example, the ratio of natural stone powder to polyvinyl chloride not only affects the hardness and durability of the floor, but also affects the transmission of sound; similarly, the proportion of the core layer, the wear-resistant layer and the bottom layer is related to the absorption and reflection characteristics of the sound. Then, a scaling method is used to define a scoring system with a scale of 1 to 9, where 1 represents the lowest influence and 9 represents the highest influence, and specific standards are set for each level. For example, if a design change increases the STL value by more than 5dB, it may be rated as 9; if the change is not obvious, such as less than 1dB, it is rated as 1. Then, the secondary factors of material and structural properties are scored separately. For example, the impact of changes in the ratio of natural stone powder to polyvinyl chloride on acoustic performance is examined, and corresponding scores are given according to preset standards to create an evaluation matrix in which rows and columns are Represent different secondary factors and fill in the matrix according to the scores of the scaling method. Each element represents the score of a specific secondary factor under given conditions. Mathematical methods such as eigenvalue and eigenvector theory are used to calculate the eigenvalues ​​of the secondary factor evaluation matrix. Special attention is paid to the largest eigenvalue because it represents the most significant direction of variation in the matrix, that is, the most important combination of evaluation factors. The eigenvector corresponding to the largest eigenvalue is extracted. Each component in this vector corresponds to the relative importance of a secondary factor. This eigenvector is then normalized. The purpose of normalization is to make the sum of the weights of all factors equal to 1, so that each weight represents the contribution ratio of the factor to the overall acoustic performance. Each element value in the normalized eigenvector now represents the standardized weight of each secondary acoustic evaluation factor. The weights of the secondary factors of structural and material properties are combined to obtain the combined secondary weight.

[0030] Specifically, the acoustic evaluation model is constructed, including: Construct a weight verification database, which includes the historical property parameters and historical acoustic test parameters of SPC floors; The mapping relationship between the historical attribute parameters of the SPC floor and the historical acoustic test parameters is extracted according to the weight verification database, and a number of historical acoustic response weights and historical combined secondary weights are obtained according to the mapping relationship; According to the historical acoustic response weights and the historical combined secondary weights, a deviation analysis is performed on the acoustic response weights and the combined secondary weights, and according to the deviation analysis results, the acoustic response weights and the combined secondary weights are optimized to obtain the optimization results, and an acoustic evaluation model is constructed according to the optimization results.

[0031] As a preferred embodiment of the above embodiment, by using the collected historical attribute parameters of the SPC floor, such as the type, thickness, density, etc. of the material, the acoustic test evaluation parameters include acoustic absorption rate, sound insulation performance index, etc., to build a weight verification database, and the Python Pandas library can be used to query and process the database. This database not only stores historical data, but also supports in-depth analysis of these data. A linear regression or decision tree algorithm is used to establish a mapping relationship. This mapping relationship can be obtained by training the machine learning algorithm through the establishment of a mapping model and cross-validating the trained model. Through this method, the weight of the acoustic evaluation model can be dynamically adjusted according to historical data and real-time test results. According to the extracted mapping relationship, the historical acoustic response weight and the historical combined secondary weight are obtained, and the deviation analysis is performed in combination with the historical weight distribution and the actual weight distribution. The weight can be optimized by a multi-objective optimization strategy or a genetic algorithm. The optimized weight parameters are used in combination with finite element analysis software, such as ANSYS or ABAQUS, to establish an acoustic evaluation model of the SPC floor.

[0032] Furthermore, a weight verification database is constructed, including: According to the historical attribute parameters of the SPC floor, historical material attribute information and historical structural attribute information are extracted to obtain an acoustic evaluation feature set; Use machine learning algorithms to perform deep learning on the acoustic evaluation feature set to obtain deep learning results; Determine the standard material ratio and standard structure proportion based on the deep learning results, and allocate the secondary weight of the historical combination based on the standard material ratio and standard structure proportion; Perform secondary learning on the historical combination secondary weights and historical acoustic test parameters to obtain the historical acoustic response weights, and construct a mapping relationship based on the historical combination secondary weights and the historical acoustic response weights; The mapping relationship and historical acoustic test parameters are combined and indexed to build a weight verification database.

[0033] In this embodiment, firstly, historical attribute parameters of the SPC floor such as materials are extracted, including the type of material (such as PVC content, type of additives, etc.), layer structure (such as core layer thickness, wear-resistant layer material, etc.) and acoustic performance indicators (such as sound wave absorption rate and sound insulation performance), and these data are organized into an acoustic evaluation feature set. Then, these features are analyzed using deep learning algorithms such as neural networks, and cross-validation technology can be used to fine-tune parameters to ensure that the model extracted from the historical data can accurately predict the acoustic performance. The model is trained through a machine learning framework such as TensorFlow or PyTorch, and the results of deep learning are used to determine the optimal material ratio and structure proportion. These results are then used to guide material use and process adjustments in production. Further, through a second round of learning and genetic algorithm optimization of the optimized historical combination secondary weights and actual acoustic test parameters, the weights are finely adjusted to reduce the deviation between the predicted and actual data. Finally, these optimized weights and mapping relationships are integrated into a weight verification database. This database provides direct data support for future design and production, ensuring that the evaluation and optimization of acoustic performance are more scientific and accurate through systematic data analysis and advanced machine learning technology, thereby effectively improving product quality and meeting market demand.

[0034] Further, the acoustic response weight and the combined secondary weight are subjected to deviation analysis according to the historical acoustic response weight and the historical combined secondary weight, and the acoustic response weight combined secondary weight is optimized according to the deviation analysis result, including: The historical acoustic response weight and the historical combined secondary weight are respectively used as standard values, and the acoustic response weight and the combined secondary weight are respectively used as actual values, and the deviation analysis is respectively performed using the loss function to obtain the deviation analysis results; According to the deviation results, the main deviation factors are identified, the weights of the main deviation factors are re-evaluated, and the loss function is used again to perform deviation analysis based on the re-evaluation results to obtain the secondary deviation analysis results; Iterative optimization is performed according to the results of the secondary deviation analysis to obtain the optimization results.

[0035] Specifically, first, extract historical acoustic response weights and historical combined secondary weights from the weight verification database, set these historical weights as the benchmark standard values ​​for the evaluation model, and at the same time, collect the acoustic response weights and combined secondary weights of the current production batch, use them as actual values ​​for the current deviation analysis, and use mathematical loss functions, such as mean square error (MSE) or mean absolute error (MAE), to quantify the deviation between the historical weights (standard values) and the actual measured weights (actual values). According to the results of the loss function, identify the acoustic response weights and combined secondary weight factors that cause the maximum deviation, conduct in-depth analysis of these major deviation factors, and explore the possible reasons behind them, such as changes in material properties. , deviations in the production process, etc., based on the identified main deviation factors, the weights of these factors are accurately re-evaluated, and the reasonable values ​​of these weights are recalculated taking into account the actual production conditions and historical performance data. After re-adjusting the weights, the deviation analysis is performed again using the same loss function to check the effect of the adjusted weights to ensure that the deviation can be significantly reduced through adjustment. According to the results of the secondary deviation analysis, if unacceptable deviations still exist, the steps of deviation identification and weight adjustment are repeated. This iterative process continues until the preset deviation threshold is reached or the deviation is no longer significantly reduced. After multiple rounds of iterations, a set of optimized acoustic response weights and combined secondary weights are finally obtained, and the weight optimization results are obtained.

[0036] Furthermore, the deviation analysis performed using the loss function includes: ; in, Indicates the actual value, Indicates the standard value, is the number of acoustic response weights or combined secondary weights, Indicates the deviation analysis results.

[0037] As a preferred embodiment of the above, according to the actual value and the standard value defined in the deviation analysis, the square error loss function is selected to perform the deviation analysis. This function calculates the square of the difference between the actual value and the standard value. The square error loss function is selected because it imposes a higher penalty on large deviations, which helps to clarify significant deviations in the weights. All weights are iterated, the difference between each pair of actual values ​​and standard values ​​is calculated, and the squares of these differences are summed to obtain the total deviation. The calculation of the total deviation provides a quantitative deviation measure for the data. The total deviation calculated by the loss function is analyzed to determine which weights have deviations that exceed the acceptable range. These weights are marked as major deviation factors and require further adjustment and optimization.

[0038] Embodiment 2: Based on the same inventive concept as the method for evaluating the acoustic performance of an SPC floor noise-proof structure in the aforementioned embodiment, the present invention further provides an SPC floor noise-proof structure acoustic performance evaluation system, the system comprising: The evaluation factor setting module collects the acoustic structural properties of the SPC floor and sets up several structural acoustic evaluation factors according to the acoustic performance evaluation requirements of the SPC floor. The acoustic structural properties include the properties of the noise-proof material and the noise-proof structure. A multi-weight assignment module assigns acoustic response weights to several structural acoustic assessment factors respectively, and assigns combined secondary weights to several structural acoustic assessment factors respectively according to acoustic construction properties; The model building and output module builds an acoustic evaluation model and outputs the structural acoustic performance evaluation results based on the acoustic evaluation model combined with the acoustic response weights and combined secondary weights.

[0039] The above adjustment system in the present invention can effectively implement the acoustic performance evaluation method of the SPC floor noise-proof structure, and the technical effects that can be achieved are as described in the above embodiments, which will not be repeated here.

[0040] Specifically, the model building and output modules include: A weight verification database construction unit is used to construct a weight verification database, which includes historical attribute parameters and historical acoustic test parameters of the SPC floor; The historical weight data extraction unit extracts the mapping relationship between the historical attribute parameters of the SPC floor and the historical acoustic test parameters according to the weight verification database, and obtains a number of historical acoustic response weights and historical combined secondary weights according to the mapping relationship; The weight deviation analysis optimization unit performs deviation analysis on the acoustic response weight and the combined secondary weight according to the historical acoustic response weight and the historical combined secondary weight, optimizes the acoustic response weight and the combined secondary weight according to the deviation analysis result, obtains the optimization result, and constructs the acoustic evaluation model according to the optimization result.

[0041] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the optimization effects corresponding to the method in Example 1, which will not be repeated here.

[0042] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined therein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for evaluating the acoustic performance of SPC floor noise-proof structure, characterized in that: The method comprises: Acoustic structural properties of the SPC floor are collected, and several structural acoustic evaluation factors are established according to the acoustic performance evaluation requirements of the SPC floor, wherein the acoustic structural properties include noise-proof material properties and noise-proof structural properties; Assigning acoustic response weights to the plurality of structural acoustic assessment factors respectively, and assigning combined secondary weights to the plurality of structural acoustic assessment factors respectively according to the acoustic construction properties; An acoustic evaluation model is constructed, and a structural acoustic performance evaluation result is output according to the acoustic evaluation model in combination with the acoustic response weight and the combined secondary weight.

2. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 1 is characterized in that: Acoustic response weights are assigned to several structural acoustic assessment factors, including: Collecting historical attribute parameters of the SPC floor, allocating initial evaluation weights according to the historical attribute parameters of the SPC floor and a number of the structural acoustic evaluation factors, and calculating the initial total score according to the initial evaluation weights; Based on the initial evaluation weight, mark any of the structural acoustic evaluation factors as a variable factor, and mark the other structural acoustic evaluation factors as constant factors, respectively increase or decrease the initial evaluation weight of the variable factor, and calculate a total score, compare the total score with the initial total score, and obtain a change in the total score; Cancel the mark of the variable factor, select any of the constant factors to be marked as the variable factor, use the canceled variable factor as the constant factor again, increase or decrease the initial evaluation weight of the variable factor respectively, calculate the total score, and compare it with the initial total score to obtain the change in the total score; Repeating marking the variable factors until all the structural acoustics assessment factors serve as the variable factors, and obtaining the total score change corresponding to the variable factors; According to the total score change, as the influence of the plurality of structural acoustics assessment factors on the structural acoustics performance assessment, acoustic response weights are allocated according to the influence.

3. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 2 is characterized in that: Calculating the initial total score according to the initial evaluation weight includes: S1: collecting historical acoustic test parameters, performing performance analysis on the historical acoustic test parameters according to the control variable method, obtaining performance analysis results, assigning scores to several structural acoustic evaluation factors based on the performance analysis results, and obtaining individual scores corresponding to several structural acoustic evaluation factors; S2: integrating the individual scores corresponding to the plurality of structural acoustic assessment factors with the initial assessment weights respectively to obtain the individual contributions corresponding to the structural acoustic assessment factors, and combining the individual contributions corresponding to the plurality of structural acoustic assessment factors to obtain an initial total score; The total score change is obtained by calculating the total score according to steps S1 and S2, and calculating the difference between the total score and the initial total score to obtain the total score change.

4. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 2 is characterized in that: According to the acoustic construction properties, a plurality of structural acoustic assessment factors are respectively assigned combined secondary weights, including: Establishing a number of secondary acoustic evaluation factors according to the properties of the noise-proof material and the noise-proof structure, wherein the secondary acoustic evaluation factors include the ratio of natural stone powder to polyvinyl chloride, and the proportion of the core layer, the wear-resistant layer, and the bottom layer; The acoustic influence standard is set by using a scaling method, and the ratio of the natural stone powder to the polyvinyl chloride and the proportion of the core layer, the wear-resistant layer and the bottom layer are evaluated according to the acoustic influence standard to obtain an evaluation value, wherein the evaluation is to evaluate the material properties of the natural stone powder and the polyvinyl chloride and the structural properties of the proportion of the core layer, the wear-resistant layer and the bottom layer respectively; constructing secondary factor evaluation matrices according to the evaluation values, respectively, wherein the rows and columns of the secondary factor evaluation matrix are the secondary acoustic evaluation factors, and the element values ​​in the matrix are the evaluation values; Calculating the maximum eigenvalue of the secondary factor evaluation matrix by a mathematical method, obtaining a corresponding eigenvector according to the maximum eigenvalue, normalizing the eigenvector to obtain a normalized eigenvector, wherein several element values ​​in the normalized eigenvector represent secondary evaluation weights of the secondary acoustic evaluation factor; The secondary evaluation weight of the noise-proofing material property is combined with the secondary evaluation weight of the noise-proofing structure property to obtain a combined secondary weight.

5. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 4 is characterized in that: Build an acoustic assessment model, including: Constructing a weight verification database, wherein the weight verification database includes the historical attribute parameters of the SPC floor and the historical acoustic test parameters; Extracting a mapping relationship between the historical attribute parameters of the SPC floor and the historical acoustic test parameters according to the weight verification database, and obtaining a number of historical acoustic response weights and historical combined secondary weights according to the mapping relationship; According to the historical acoustic response weight and the historical combined secondary weight, a deviation analysis is performed on the acoustic response weight and the combined secondary weight, and according to the deviation analysis result, the acoustic response weight and the combined secondary weight are optimized to obtain an optimization result, and an acoustic evaluation model is constructed according to the optimization result.

6. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 5 is characterized in that: Build a weight verification database, including: Extracting historical material attribute information and historical structural attribute information according to the historical attribute parameters of the SPC floor to obtain an acoustic evaluation feature set; Using a machine learning algorithm to perform deep learning on the acoustic evaluation feature set to obtain a deep learning result; Determine the standard material ratio and the standard structure proportion according to the deep learning result, and allocate the secondary weight of the historical combination according to the standard material ratio and the standard structure proportion; Performing secondary learning on the historical combined secondary weights and the historical acoustic test parameters to obtain historical acoustic response weights, and constructing a mapping relationship based on the historical combined secondary weights and the historical acoustic response weights; The mapping relationship and historical acoustic test parameters are combined and indexed to construct a weight verification database.

7. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 5 is characterized in that: The method further comprises: performing a deviation analysis on the acoustic response weight and the combined secondary weight according to the historical acoustic response weight and the historical combined secondary weight, and optimizing the acoustic response weight and the combined secondary weight according to the deviation analysis result, including: The historical acoustic response weight and the historical combined secondary weight are respectively used as standard values, and the acoustic response weight and the combined secondary weight are respectively used as actual values, and the deviation analysis is performed respectively using the loss function to obtain the deviation analysis result; Identify the main deviation factors according to the deviation results, re-evaluate the weights of the main deviation factors, and use the loss function again to perform deviation analysis according to the re-evaluation results to obtain secondary deviation analysis results; Iterative optimization is performed according to the secondary deviation analysis result to obtain an optimization result.

8. The method for evaluating the acoustic performance of the SPC floor noise-proof structure according to claim 7 is characterized in that: The deviation analysis performed using loss functions includes: ； in, represents the actual value, Indicates the standard value, is the number of acoustic response weights or combined secondary weights, The deviation analysis result is shown. 9.SPC floor noise-proof structural acoustic performance evaluation system, characterized by: The system comprises: An evaluation factor setting module collects the acoustic structural properties of the SPC floor and sets up a number of structural acoustic evaluation factors according to the acoustic performance evaluation requirements of the SPC floor, wherein the acoustic structural properties include noise-proof material properties and noise-proof structural properties; A multi-weight assignment module is configured to assign acoustic response weights to the plurality of structural acoustic assessment factors respectively, and to assign combined secondary weights to the plurality of structural acoustic assessment factors respectively according to the acoustic construction properties; The model building and output module builds an acoustic evaluation model, and outputs a structural acoustic performance evaluation result according to the acoustic evaluation model in combination with the acoustic response weight and the combined secondary weight.

10. The SPC floor noise-proof structural acoustic performance evaluation system according to claim 9, characterized in that: The model building and output module includes: A weight verification database construction unit is used to construct a weight verification database, wherein the weight verification database includes the historical attribute parameters of the SPC floor and the historical acoustic test parameters; A historical weight data extraction unit extracts a mapping relationship between the historical attribute parameters of the SPC floor and the historical acoustic test parameters according to the weight verification database, and obtains a number of historical acoustic response weights and historical combined secondary weights according to the mapping relationship; The weight deviation analysis and optimization unit performs deviation analysis on the acoustic response weight and the combined secondary weight according to the historical acoustic response weight and the historical combined secondary weight, optimizes the acoustic response weight and the combined secondary weight according to the deviation analysis result, obtains the optimization result, and constructs the acoustic evaluation model according to the optimization result.

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