Sound insulation foam production optimization method and system based on machine learning

Abstract

The invention discloses a sound insulation foam production optimization method and system based on machine learning, and relates to the related technical field of building protection, and the method comprises the steps: carrying out the sound insulation detection based on an acoustic camera, determining a sound wave spectrum, and combining a building sound insulation standard and a sound insulation coefficient. And establishing an optimization decision module, wherein the optimization decision module comprises an optimization decision module and an acoustic evaluation module. And based on the sound wave spectrum and the sound insulation coefficient, product optimization decision and sound insulation evaluation are carried out in combination with an optimization decision module, a product optimization strategy is determined, optimization dimensions comprise sound insulation optimization and lightweight optimization, and sound insulation optimization comprises the sound insulation coefficient. And carrying out process conversion on the product optimization strategy by taking the initial process flow as a benchmark, and determining an optimized production process. And based on the optimized production process, optimized production management of the sound insulation foam is carried out. The technical problem that in the prior art, a sound insulation foam production process is low in intellectualization, and consequently the sound insulation effect of a sound insulation foam product in different frequency bands and complex environments is poor is solved.

Description

Technical Field

[0001] The present application relates to fields related to building protection, and specifically to a method and system for optimizing the production of sound insulation foam based on machine learning. Background Art

[0002] Currently, sound insulation foam has been widely used in the construction field, especially in residential, commercial buildings and industrial facilities. However, most of the existing sound insulation foams are made of single materials or simple composite materials. There are limitations in the design and optimization of sound insulation foams, and it is difficult to provide ideal sound insulation effects in different frequency bands and complex environments in practical applications.

[0003] Therefore, in the prior art, the intelligent production process of the sound insulation foam is relatively low, resulting in the technical problem that the sound insulation foam products have poor sound insulation effects in different frequency bands and complex environments. Summary of the invention

[0004] This application solves the technical problem that the sound insulation foam production process in the prior art is not very intelligent, resulting in poor sound insulation effect of the sound insulation foam products in different frequency bands and complex environments, by providing a sound insulation foam production optimization method and system based on machine learning. The sound insulation performance of the sound insulation material is detected by an acoustic camera, and combined with an optimization decision module, an accurate evaluation of the sound insulation performance is achieved, and the sound insulation foam optimization design scheme is obtained based on the evaluation results, thereby improving the sound insulation effect of the sound insulation foam in different frequency bands and complex environments.

[0005] The present application provides a method for optimizing the production of sound insulation foam based on machine learning, the method comprising: based on an acoustic camera, performing sound insulation detection to determine a sound wave spectrum, wherein the sound insulation detection includes the sound insulation of the building itself and the sound insulation of the foam. Based on the sound wave spectrum, combined with the building sound insulation standard, a sound insulation performance evaluation is performed to determine the sound insulation coefficient, and the sound insulation coefficient has an attenuation processing based on the gap tolerance range. An optimization decision module is constructed, and the optimization decision module includes an optimization decision module and an acoustic evaluation module. Based on the sound wave spectrum and the sound insulation coefficient, product optimization decisions and sound insulation evaluations are performed in combination with the optimization decision module to determine the product optimization strategy, wherein the optimization dimensions include sound insulation optimization and lightweight optimization, and the sound insulation optimization includes the sound insulation coefficient. Based on the initial process flow, the product optimization strategy is converted into a process to determine the optimized production process. Based on the optimized production process, optimized production management of sound insulation foam is performed.

[0006] In an implementation, the sound insulation performance evaluation to determine the sound insulation coefficient includes: traversing the sound wave spectrum, performing spatial layer division, and determining multiple local sound insulation layers. Traversing the multiple local sound insulation layers, and calculating multiple sound insulation coefficients based on the ratio of incident sound waves to absorbed sound waves, wherein the multiple sound insulation coefficients correspond to the multiple local sound insulation layers one by one. Calculating the mean of the multiple sound insulation coefficients to determine the sound insulation coefficient, wherein the sound insulation coefficient is marked with an optimization amount, and the optimization amount is determined based on the sound insulation coefficient and the building sound insulation standard.

[0007] In the implementation, the sound insulation coefficient includes attenuation processing based on the gap tolerance range, including: based on the construction fixing method, analyzing the sound insulation attenuation based on the construction gap, determining the gap tolerance range, and the gap tolerance range is the maximum value of the multi-mode attenuation that meets the construction standard. Based on the sound insulation attenuation, the sound insulation coefficient is attenuated and compensated.

[0008] In the implementation method, before making product optimization decisions and sound insulation evaluation, it includes: determining the product characteristics of sound insulation foam and functional indicators based on sound insulation performance. Interactive sound insulation test records, through variable-limited clustering, mining influence relationships, the influence relationship is the trend relationship between at least one product characteristic and related functional indicators. With the product characteristics as matrix rows and the functional indicators as matrix columns, a relationship matrix is ​​established in combination with the influence relationship.

[0009] In the implementation method, product optimization decision and sound insulation evaluation are performed, including: the product characteristics include structural characteristics and morphological characteristics, and the structural characteristics include fiber structure and composite structure. With the relationship matrix as a constraint, based on the product characteristics, the sound insulation foam is optimized based on finite element analysis, and fitness is checked through sound insulation evaluation to determine the product optimization strategy.

[0010] In the implementation, the sound insulation foam is optimized based on finite element analysis, including: traversing the product characteristics, randomly determining a first number of optimization targets, the first number being a positive integer less than or equal to the total amount of the structural characteristics. Based on the optimization target, with the relationship matrix as a constraint, a random perturbation based on the initial product characteristics of the sound insulation foam is performed to determine a first strategy set. The optimization target quantity iteration and optimization iteration are performed until the convergence condition is met, and the optimization strategy set is determined, wherein the quantity iteration has no correlation.

[0011] In an implementation, the process conversion of the product optimization strategy includes: traversing the initial process flow, mapping the product optimization strategy to associated process nodes, and determining multiple mapping groups, wherein each optimization strategy point has at least one associated process node. Based on the multiple mapping groups, the process nodes of the optimization strategy points are allocated to determine the optimized production process, wherein the allocation ratio is the node correlation.

[0012] The present application also provides a sound insulation foam production optimization system based on machine learning, the system comprising:

[0013] The sound wave detection module is used to perform sound insulation detection based on an acoustic camera to determine the sound wave spectrum, wherein the sound insulation detection includes the building's own sound insulation and foam sound insulation.

[0014] The sound insulation coefficient acquisition module is used to determine the sound insulation coefficient by evaluating the sound insulation performance based on the sound wave spectrum and in combination with the building sound insulation standard. The sound insulation coefficient has attenuation processing based on the gap tolerance range.

[0015] The optimization module is used to build an optimization decision module, which includes an optimization decision module and an acoustic evaluation module.

[0016] An iterative module is used to make product optimization decisions and sound insulation evaluation based on the sound wave spectrum and the sound insulation coefficient in combination with the optimization decision module to determine the product optimization strategy, wherein the optimization dimensions include sound insulation optimization and lightweight optimization, and the sound insulation optimization includes the sound insulation coefficient.

[0017] The production process determination module is used to perform process conversion on the product optimization strategy based on the initial process flow to determine the optimized production process.

[0018] The production management module is used to optimize the production management of the sound insulation foam based on the optimized production process.

[0019] The method and system for optimizing the production of sound insulation foam based on machine learning proposed in this application are proposed to perform sound insulation detection based on an acoustic camera to determine a sound wave spectrum, wherein the sound insulation detection includes the sound insulation of the building itself and the sound insulation of the foam. Based on the sound wave spectrum, combined with the building sound insulation standard, a sound insulation performance evaluation is performed to determine the sound insulation coefficient, and the sound insulation coefficient has an attenuation processing based on the gap tolerance range. An optimization decision module is built, and the optimization decision module includes an optimization decision module and an acoustic evaluation module. Based on the sound wave spectrum and the sound insulation coefficient, the optimization decision module is combined to make product optimization decisions and sound insulation evaluations to determine the product optimization strategy, wherein the optimization dimensions include sound insulation optimization and lightweight optimization, and the sound insulation optimization includes the sound insulation coefficient. Based on the initial process flow, the product optimization strategy is converted into a process to determine the optimized production process. Based on the optimized production process, the optimized production management of the sound insulation foam is performed. The technical problem that the sound insulation foam production process in the prior art is low in intelligence, resulting in poor sound insulation effect of the sound insulation foam products in different frequency bands and complex environments is solved. The sound insulation performance of the sound insulation material is tested by an acoustic camera, and combined with the optimization decision-making module, an accurate evaluation of the sound insulation performance is achieved. Based on the evaluation results, the optimization design plan of the sound insulation foam is obtained, which improves the sound insulation effect of the sound insulation foam in different frequency bands and complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0021] Figure 1 A schematic diagram of a process flow of a sound insulation foam production optimization method based on machine learning provided in an embodiment of the present application;

[0022] Figure 2 Schematic diagram of the structure of the sound insulation foam production optimization system based on machine learning provided in an embodiment of the present application.

[0023] Explanation of the accompanying drawings: sound wave detection module 11, sound insulation coefficient acquisition module 12, optimization module 13, iteration module 14, production process determination module 15, production management module 16. DETAILED DESCRIPTION

[0024] 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.

[0025] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0026] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0027] The present application embodiment provides a method and system for optimizing the production of sound insulation foam based on machine learning, such as Figure 1 As shown, the method includes:

[0028] Based on the acoustic camera, sound insulation detection is carried out to determine the sound wave spectrum, where the sound insulation detection includes the building's own sound insulation and foam sound insulation.

[0029] Based on the sound wave spectrum and in combination with the building sound insulation standard, a sound insulation performance evaluation is performed to determine the sound insulation coefficient, and the sound insulation coefficient has an attenuation processing based on the gap tolerance range.

[0030] An optimization decision module is constructed, wherein the optimization decision module includes an optimization decision module and an acoustic evaluation module.

[0031] The building itself and the foam are sound-insulated by an acoustic camera to obtain a sound wave spectrum, which contains the sound wave attenuation information of the building's own sound insulation and the foam sound insulation in different frequency bands, and is used to reflect the sound insulation effect of each material. Subsequently, based on the sound wave spectrum and in combination with the building sound insulation standard, a sound insulation performance evaluation is performed to determine the sound insulation coefficient. The sound insulation coefficient has an attenuation process based on the gap tolerance range, that is, the sound insulation coefficient has completed the attenuation compensation process. Further, an optimization decision module is constructed, and the optimization decision module includes an optimization decision module and an acoustic evaluation module. Among them, the optimization decision module is used to obtain a product optimization strategy based on the product characteristics with the relationship matrix as a constraint. The acoustic evaluation module is constructed based on a neural network model, and the sample sound insulation foam product characteristics and the corresponding sample sound insulation test results and sample weight test results are obtained through historical optimization records and experimental test records. The sample sound insulation test results include the sound insulation coefficient. An acoustic training data set is constructed based on the product characteristics of the sample sound insulation foam and the corresponding sample sound insulation test results and sample weight test results. The neural network model is supervised and trained using the acoustic training data set until the output accuracy of the model meets the preset accuracy requirements, and then an acoustic evaluation module is obtained.

[0032] The method provided in the embodiment of the present application also includes:

[0033] The sound wave spectrum is traversed, spatial layer division is performed, and a plurality of local sound insulation layers are determined.

[0034] The multiple local sound insulation layers are traversed, and multiple sound insulation coefficients are calculated based on the ratio of incident sound waves to absorbed sound waves, wherein the multiple sound insulation coefficients correspond to the multiple local sound insulation layers one by one.

[0035] The sound insulation coefficient is determined by performing an average calculation on the multiple sound insulation coefficients, wherein the sound insulation coefficient is marked with an optimization amount, and the optimization amount is determined based on the sound insulation coefficient and the building sound insulation standard.

[0036] The sound insulation performance evaluation to determine the sound insulation coefficient includes: traversing the sound wave spectrum, performing spatial layer division, and determining multiple local sound insulation layers, that is, obtaining the sound wave spectrum of the sound insulation foam in different frequency bands through an acoustic camera. The sound wave spectrum is traversed, and multiple local sound insulation layers are divided according to the spatial position, and each local sound insulation layer represents the sound insulation performance of a specific position. The multiple local sound insulation layers are traversed, and based on the ratio of the incident sound wave to the absorbed sound wave, the incident sound wave is the sound wave input during the test, and the absorbed sound wave is the sound wave collected after passing through the local sound insulation layer, multiple sound insulation coefficients are calculated, and the sound insulation coefficient is the ratio of the intensity of the incident sound wave to the intensity of the absorbed sound wave, and the multiple sound insulation coefficients correspond one-to-one to the multiple local sound insulation layers. Furthermore, the mean of the multiple sound insulation coefficients is calculated to determine the sound insulation coefficient. The sound insulation coefficient is marked with an optimization amount. The optimization amount is determined based on the sound insulation coefficient and the building sound insulation standard. The optimization amount is the difference between the building sound insulation standard and the sound insulation coefficient, so as to ensure that the sound insulation effect of the sound insulation foam in actual application meets expectations.

[0037] The method provided in the embodiment of the present application also includes:

[0038] Based on the construction fixing method, the sound insulation attenuation based on the construction gap is analyzed to determine the gap tolerance range, and the gap tolerance range is the maximum value of the multi-mode attenuation that meets the construction standards.

[0039] Based on the sound insulation attenuation degree, the sound insulation coefficient is subjected to attenuation compensation processing.

[0040] According to the possible fixing methods that may appear during the actual construction process, the influence of construction gaps on the sound insulation performance is analyzed. Specifically, experiments or simulations can be used to measure the influence of different gap sizes caused by the fixing methods during the construction process on the sound insulation effect, and the gap tolerance range can be determined. The gap tolerance range is the maximum gap size that can be accepted during construction. The maximum value of the sound insulation attenuation under this size is the maximum value of the multi-mode attenuation, and the sound insulation attenuation is the parameter that affects the sound insulation coefficient. Furthermore, based on the sound insulation attenuation, the sound insulation coefficient is attenuated and compensated, that is, the sound insulation coefficient is compensated according to the sound insulation attenuation.

[0041] The method provided in the embodiment of the present application also includes:

[0042] Determine the product characteristics of sound insulation foam and the functional indicators based on sound insulation performance.

[0043] Interactive sound insulation test records are used to mine influence relationships by performing variable-limited clustering, where the influence relationship is a trend relationship between at least one product characteristic and related functional indicators.

[0044] A relationship matrix is ​​established by taking the product characteristics as matrix rows and the functional indicators as matrix columns and combining the influence relationships.

[0045] Before making product optimization decisions and sound insulation evaluations, it includes: determining the product characteristics of sound insulation foam, which include its structural characteristics and morphological characteristics. Structural characteristics specifically include fiber structure and composite structure, while morphological characteristics include parameters such as density and thickness of foam. Functional indicators are sound insulation coefficients of sound insulation foams, which are measured by acoustic testing instruments to record the sound wave attenuation effects of different foam samples in different frequency bands. Interactive sound insulation test records, which are the trend and change relationship between product characteristics and related functional indicators. Specifically, a variety of sound insulation test methods (such as standard test rooms, field tests, etc.) are used to record the sound insulation effects of sound insulation foam samples with different product characteristics. Through statistical software, variable-limited clustering is performed on the test data to analyze the relationship between product characteristics (such as density, thickness) and the sound insulation coefficient of sound insulation performance indicators. The influence relationship is the trend relationship between at least one product characteristic and related functional indicators. The above analysis results are expressed in matrix form, with the product characteristics as matrix rows and the functional indicators as matrix columns, and a relationship matrix is ​​established in combination with the influence relationship.

[0046] Based on the sound wave spectrum and the sound insulation coefficient, the product optimization decision and sound insulation evaluation are carried out in combination with the optimization decision module to determine the product optimization strategy, wherein the optimization dimensions include sound insulation optimization and lightweight optimization, and the sound insulation optimization includes the sound insulation coefficient.

[0047] Based on the initial process flow, the product optimization strategy is transformed into an optimized production process.

[0048] Based on the optimized production process, optimized production management of the sound insulation foam is carried out.

[0049] Based on the acoustic wave spectrum and the sound insulation coefficient, the trend relationship of product characteristics is obtained, and the product optimization decision and sound insulation evaluation are carried out in combination with the optimization decision module to determine the product optimization strategy, wherein the optimization dimensions include sound insulation optimization and lightweight optimization, and the sound insulation optimization includes the sound insulation coefficient. Further, taking the initial process flow as the optimization benchmark, the product optimization strategy is converted into a process, and the product optimization strategy is converted into a specific manufacturing process flow to determine the optimized production process. Finally, based on the optimized production process, the optimized production management of the sound insulation foam is carried out. The technical problem that the sound insulation foam production process in the prior art is low in intelligence, resulting in poor sound insulation effect of the sound insulation foam products in different frequency bands and complex environments is solved. The sound insulation performance of the sound insulation material is detected by an acoustic camera, and combined with the optimization decision module, the sound insulation performance is accurately evaluated, and the sound insulation foam optimization design scheme is obtained based on the evaluation results, which improves the sound insulation effect of the sound insulation foam in different frequency bands and complex environments.

[0050] The method provided in the embodiment of the present application also includes:

[0051] The product characteristics include structural characteristics and morphological characteristics, and the structural characteristics include fiber structure and composite structure.

[0052] Taking the relationship matrix as a constraint and based on the product characteristics, the sound insulation foam is optimized based on finite element analysis, and fitness is calibrated through sound insulation evaluation to determine the product optimization strategy.

[0053] Carry out product optimization decision and sound insulation evaluation, including: the product characteristics include structural characteristics and morphological characteristics, the structural characteristics include fiber structure and composite structure, fiber structure such as the arrangement of fibers inside the foam, such as straight fibers, staggered fibers, etc. Composite structure such as whether the foam contains a multi-layer structure, such as single-layer foam, double-layer foam, etc. Subsequently, the relationship matrix is ​​used as a constraint, and based on the product characteristics, the sound insulation foam is optimized based on finite element analysis, and the fitness is calibrated through the acoustic evaluation module. The fitness is obtained by weighted summation of the sound insulation result output by the acoustic evaluation module and the difference between the weight of the initial process flow and the weight result output by the acoustic evaluation module. Based on the fitness calculation result, the larger the fitness calculation result, the better the corresponding product strategy, and the product optimization strategy is determined.

[0054] The method provided in the embodiment of the present application also includes:

[0055] The product characteristics are traversed, and a first number of optimization targets are randomly determined, where the first number is a positive integer less than or equal to the total amount of the structural characteristics.

[0056] Based on the optimization objective and with the relationship matrix as a constraint, random perturbations are performed based on the initial product characteristics of the sound insulation foam to determine a first strategy set.

[0057] Perform optimization target quantity iteration and optimization iteration until the convergence condition is met and determine the optimization strategy set, where the quantity iteration has no correlation.

[0058] The sound insulation foam is optimized based on finite element analysis, including: traversing the product characteristics, randomly determining a first number of optimization targets in the product characteristics, the optimization targets are characteristic categories included in the product characteristics, such as morphological characteristics including density and thickness characteristics of the foam, the first number is a positive integer less than or equal to the total amount of the structural characteristics, and iterative analysis is performed for different target groups of different numbers each time to ensure randomness. Subsequently, based on the optimization target and with the relationship matrix as a constraint, a random perturbation is performed based on the initial product characteristics of the sound insulation foam, and the selected density is adjusted slightly according to the preferred direction in the relationship matrix, such as changing the density from 100kg / m 3 Adjusted to 105kg / m 3, determine the first strategy set, perform sound insulation evaluation of the first strategy set through the acoustic evaluation module, and perform iterative optimization based on the evaluation results. The optimization direction is the direction of better sound insulation coefficient of the relationship matrix and the optimization direction of lighter mass. Subsequently, the optimization target quantity iteration and optimization iteration are performed until the convergence condition is met. The convergence condition is that when the preset number of iterations is met, the optimization is completed and the optimization strategy set is determined. The optimization strategy set is the strategy set corresponding to the best sound insulation coefficient obtained after the iterative optimization is completed, and the number of iterations is irrelevant.

[0059] The method provided in the embodiment of the present application also includes:

[0060] The initial process flow is traversed, and the product optimization strategy is mapped to associated process nodes to determine a plurality of mapping groups, wherein each optimization strategy point has at least one associated process node.

[0061] Based on the multiple mapping groups, the process nodes of the optimization strategy points are allocated to determine the optimized production process, wherein the allocation ratio is the node correlation.

[0062] Traverse the initial process flow, and map the associated process nodes of the product optimization strategy, that is, obtain the strategy that needs to optimize the initial process flow in the product optimization strategy, and determine the associated process nodes corresponding to the strategy, and determine multiple mapping groups, each mapping group corresponds to an optimization strategy category, wherein each optimization strategy point has at least one associated process node. And each strategy category in the optimization strategy and the associated process node are preset corresponding relationships, and the corresponding relationships are stored in the optimization corresponding database, in which each optimization strategy category adjustment parameter corresponds to one or more associated process node adjustment parameters. Finally, based on the multiple mapping groups, the process node allocation of the optimization strategy point is performed, and multiple process nodes will have an impact on the quality of a certain feature, and the impact of the process node on the feature is a preset allocation ratio, based on the node process parameters corresponding to the multiple mapping groups are added and multiplied by the preset allocation ratio, and the preset allocation ratio is the allocation ratio preset by the technician based on the node relevance, so as to determine the specific node process parameters and determine the optimized production process, wherein the allocation ratio is the node relevance.

[0063] In the above, refer to Figure 1 The method for optimizing the production of sound insulation foam based on machine learning according to an embodiment of the present invention is described in detail. Figure 2 A sound insulation foam production optimization system based on machine learning according to an embodiment of the present invention is described.

[0064] The sound insulation foam production optimization system based on machine learning according to the embodiment of the present invention solves the technical problem that the sound insulation foam production process in the prior art is not very intelligent, resulting in poor sound insulation effects of sound insulation foam products in different frequency bands and complex environments. The sound insulation performance of the sound insulation material is detected by an acoustic camera, and combined with the optimization decision module, an accurate evaluation of the sound insulation performance is achieved, and the sound insulation foam optimization design scheme is obtained based on the evaluation results, thereby improving the sound insulation effect of the sound insulation foam in different frequency bands and complex environments. The sound insulation foam production optimization system based on machine learning includes: a sound wave detection module 11, a sound insulation coefficient acquisition module 12, an optimization module 13, an iteration module 14, a production process determination module 15, and a production management module 16.

[0065] The sound wave detection module 11 is used to perform sound insulation detection based on an acoustic camera to determine a sound wave spectrum, wherein the sound insulation detection includes building self-sound insulation and foam sound insulation.

[0066] The sound insulation coefficient acquisition module 12 is used to determine the sound insulation coefficient by evaluating the sound insulation performance based on the sound wave spectrum and in combination with the building sound insulation standard. The sound insulation coefficient has an attenuation process based on the gap tolerance range.

[0067] The optimization module 13 is used to build an optimization decision module, which includes an optimization decision module and an acoustic evaluation module.

[0068] The iteration module 14 is used to make product optimization decisions and sound insulation evaluation based on the sound wave spectrum and the sound insulation coefficient in combination with the optimization decision module to determine the product optimization strategy, wherein the optimization dimensions include sound insulation optimization and lightweight optimization, and the sound insulation optimization includes the sound insulation coefficient.

[0069] The production process determination module 15 is used to perform process conversion on the product optimization strategy based on the initial process flow to determine the optimized production process.

[0070] The production management module 16 is used to perform optimized production management of the sound insulation foam based on the optimized production process.

[0071] The specific configuration of the sound insulation coefficient acquisition module 12 will be described in detail below. The sound insulation coefficient acquisition module 12 may further include: the sound insulation performance evaluation to determine the sound insulation coefficient includes: traversing the sound wave spectrum, performing spatial layer division, and determining multiple local sound insulation layers. Traversing the multiple local sound insulation layers, based on the ratio of incident sound waves to absorbed sound waves, calculating multiple sound insulation coefficients, the multiple sound insulation coefficients corresponding to the multiple local sound insulation layers one by one. The multiple sound insulation coefficients are averaged to determine the sound insulation coefficient, the sound insulation coefficient is marked with an optimization amount, and the optimization amount is determined based on the sound insulation coefficient and the building sound insulation standard.

[0072] The specific configuration of the sound insulation coefficient acquisition module 12 will be described in detail below. The sound insulation coefficient acquisition module 12 further includes: based on the construction fixing method, analyzing the sound insulation attenuation based on the construction gap, determining the gap tolerance range, and the gap tolerance range is the maximum value of the multi-mode attenuation that meets the construction standard. Based on the sound insulation attenuation, the sound insulation coefficient is attenuated and compensated.

[0073] The specific configuration of the optimization module 13 will be described in detail below. The optimization module 13 may further include: before making product optimization decisions and sound insulation evaluations, including: determining the product characteristics of the sound insulation foam and the functional indicators based on the sound insulation performance. Interactive sound insulation test records, by performing variable-limited clustering, mining the influence relationship, the influence relationship is the trend relationship between at least one product characteristic and the relevant functional indicators. With the product characteristics as the matrix rows and the functional indicators as the matrix columns, a relationship matrix is ​​established in combination with the influence relationship.

[0074] The specific configuration of the iteration module 14 will be described in detail below. The iteration module 14 further includes: making product optimization decisions and sound insulation evaluation, including: the product characteristics include structural characteristics and morphological characteristics, and the structural characteristics include fiber structure and composite structure. Taking the relationship matrix as a constraint, based on the product characteristics, the sound insulation foam is optimized based on finite element analysis, and fitness is checked through sound insulation evaluation to determine the product optimization strategy.

[0075] The specific configuration of the iteration module 14 will be described in detail below. The iteration module 14 further includes: performing optimization based on finite element analysis on the sound insulation foam, including: traversing the product characteristics, randomly determining a first number of optimization targets, and the first number is a positive integer less than or equal to the total amount of the structural characteristics. Based on the optimization target, with the relationship matrix as a constraint, a random perturbation is performed based on the initial product characteristics of the sound insulation foam to determine a first strategy set. Perform optimization target quantity iteration and optimization iteration until the convergence condition is met, and determine the optimization strategy set, wherein the quantity iteration has no correlation.

[0076] The specific configuration of the production process determination module 15 will be described in detail below. The production process determination module 15 further includes: performing process conversion on the product optimization strategy, including: traversing the initial process flow, mapping the product optimization strategy to associated process nodes, and determining multiple mapping groups, wherein each optimization strategy point has at least one associated process node. Based on the multiple mapping groups, the process nodes of the optimization strategy points are allocated to determine the optimized production process, wherein the allocation ratio is the node correlation.

[0077] The sound insulation foam production optimization system based on machine learning provided in the embodiment of the present invention can execute the sound insulation foam production optimization method based on machine learning provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0078] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or the server, and the various units and modules included are only divided according to functional logic, but are not limited to the above divisions, as long as the corresponding functions can be achieved. In addition, the specific names of the functional units are only for the convenience of distinguishing from each other, and are not used to limit the scope of protection of the present invention.

[0079] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for optimizing the production of sound insulation foam based on machine learning, characterized in that: The method comprises: Based on the acoustic camera, sound insulation detection is carried out to determine the sound wave spectrum, where the sound insulation detection includes the building's own sound insulation and foam sound insulation; Based on the sound wave spectrum and in combination with the building sound insulation standard, a sound insulation performance evaluation is performed to determine a sound insulation coefficient, wherein the sound insulation coefficient has an attenuation process based on a gap tolerance range; Building an optimization decision module, wherein the optimization decision module includes an optimization decision module and an acoustic evaluation module; Based on the sound wave spectrum and the sound insulation coefficient, the optimization decision module is combined to make product optimization decisions and sound insulation evaluation, and determine the product optimization strategy, wherein the optimization dimensions include sound insulation optimization and lightweight optimization, and the sound insulation optimization includes the sound insulation coefficient; Based on the initial process flow, the product optimization strategy is converted into a process to determine the optimized production process; Based on the optimized production process, optimized production management of the sound insulation foam is carried out.

2. The method for optimizing the production of sound insulation foam based on machine learning according to claim 1, characterized in that: The sound insulation performance evaluation and determination of the sound insulation coefficient include: Traversing the sound wave spectrum, dividing the space layer, and determining a plurality of local sound insulation layers; Traversing the multiple local sound insulation layers, and calculating multiple sound insulation coefficients based on the ratio of incident sound waves to absorbed sound waves, wherein the multiple sound insulation coefficients correspond to the multiple local sound insulation layers one by one; The sound insulation coefficient is determined by performing an average calculation on the multiple sound insulation coefficients, wherein the sound insulation coefficient is marked with an optimization amount, and the optimization amount is determined based on the sound insulation coefficient and the building sound insulation standard.

3. The method for optimizing the production of sound insulation foam based on machine learning as claimed in claim 2, characterized in that: The sound insulation coefficient includes attenuation processing based on the gap tolerance range, including: Based on the construction fixing method, the sound insulation attenuation based on the construction gap is analyzed to determine the gap tolerance range, and the gap tolerance range is the maximum value of the multi-mode attenuation that meets the construction standard; Based on the sound insulation attenuation degree, the sound insulation coefficient is subjected to attenuation compensation processing.

4. The method for optimizing the production of sound insulation foam based on machine learning according to claim 1, characterized in that: Before making product optimization decisions and sound insulation assessments, include: Determine the product characteristics of sound insulation foam and the functional indicators based on sound insulation performance; Interactive sound insulation test records, by performing variable-limited clustering, mining the influence relationship, the influence relationship is the trend relationship between at least one product characteristic and related functional indicators; A relationship matrix is ​​established by taking the product characteristics as matrix rows and the functional indicators as matrix columns and combining the influence relationships.

5. The method for optimizing the production of sound insulation foam based on machine learning according to claim 4, characterized in that: Make product optimization decisions and sound insulation assessments, including: The product characteristics include structural characteristics and morphological characteristics, and the structural characteristics include fiber structure and composite structure; Taking the relationship matrix as a constraint and based on the product characteristics, the sound insulation foam is optimized based on finite element analysis, and fitness is calibrated through sound insulation evaluation to determine the product optimization strategy.

6. The method for optimizing the production of sound insulation foam based on machine learning according to claim 5, characterized in that: The sound insulation foam is optimized based on finite element analysis, including: Traversing the product characteristics, randomly determining a first number of optimization targets, where the first number is a positive integer less than or equal to the total amount of the structural characteristics; Based on the optimization objective and taking the relationship matrix as a constraint, a random perturbation based on the initial product characteristics of the sound insulation foam is performed to determine a first strategy set; Perform optimization target quantity iteration and optimization iteration until the convergence condition is met and determine the optimization strategy set, where the quantity iteration has no correlation.

7. The method for optimizing the production of sound insulation foam based on machine learning according to claim 1, characterized in that: Process conversion of the product optimization strategy, including: Traversing the initial process flow, mapping the product optimization strategy to associated process nodes, and determining a plurality of mapping groups, wherein each optimization strategy point has at least one associated process node; Based on the multiple mapping groups, the process nodes of the optimization strategy points are allocated to determine the optimized production process, wherein the allocation ratio is the node correlation.

8. A sound insulation foam production optimization system based on machine learning, characterized in that: The system comprises: The sound wave detection module is used to perform sound insulation detection based on an acoustic camera to determine the sound wave spectrum, wherein the sound insulation detection includes the building's own sound insulation and foam sound insulation; A sound insulation coefficient acquisition module is used to evaluate the sound insulation performance based on the sound wave spectrum and in combination with the building sound insulation standard to determine the sound insulation coefficient, wherein the sound insulation coefficient has an attenuation process based on the gap tolerance range; An optimization module is used to build an optimization decision module, wherein the optimization decision module includes an optimization decision module and an acoustic evaluation module; An iteration module, for making product optimization decisions and sound insulation evaluation based on the sound wave spectrum and the sound insulation coefficient in combination with the optimization decision module, and determining a product optimization strategy, wherein the optimization dimensions include sound insulation optimization and lightweight optimization, and the sound insulation optimization includes the sound insulation coefficient; A production process determination module is used to perform process conversion on the product optimization strategy based on the initial process flow to determine the optimized production process; The production management module is used to optimize the production management of the sound insulation foam based on the optimized production process.

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