A sound shielding body selection optimization method, device, equipment, medium and product

Abstract

The application discloses a sound shielding body selection optimization method, device, equipment, medium and product, relates to the sound shielding design field, and the method comprises the following steps: obtaining target acoustic performance requirements and constraint conditions; filtering all screen body records in a screen body measurement database according to the target acoustic performance requirements and the constraint conditions to obtain a filtering scheme; calculating the total cost of unit area material by using a cost parameter library for the filtering scheme and sorting according to the total cost of unit area material to obtain a set of recommended schemes with low cost; calculating the performance increment and cost increment of each optimization path based on an optimization direction for the recommended scheme; calculating a performance cost ratio according to the performance increment and cost increment of each optimization path; determining an optimal optimization path according to the performance cost ratio and determining an optimal screen body selection according to the optimal optimization path. The application starts from target acoustic performance, automatically searches for a lowest-cost screen body configuration, and provides an optimization direction for the lowest-cost screen body.

Description

Technical Field

[0001] This application relates to the field of acoustic shielding design, and in particular to a method, apparatus, equipment, medium and product for optimizing the selection of acoustic shielding bodies. Background Technology

[0002] The selection and design of sound barrier projects is a typical multi-objective optimization problem: it must meet acoustic performance requirements (weighted sound insulation Rw and noise reduction coefficient NRC must meet design standards) while minimizing material costs as much as possible. Currently, the selection of sound barrier panels mainly faces the following problems: First, the selection process relies heavily on the engineer's personal experience. Faced with dozens of panel materials, various back panel thicknesses, and combinations of different types and densities of sound-absorbing materials, engineers typically choose a few "common solutions" for comparison based on experience. They cannot systematically explore all feasible combinations and are prone to overlooking more cost-effective solutions.

[0003] Second, there is a lack of a systematic approach to deduce the optimal configuration from the target performance. Traditional sound barrier design software (such as CadnaA and SoundPLAN) is mainly used for sound field simulation and macroscopic parameter design of sound barriers (height, length, and location), without involving the microscopic optimization of the barrier material composition. After determining the target weighted sound insulation Rw and noise reduction coefficient NRC, engineers can only verify whether different configurations meet the requirements one by one through "forward trial and error," which is inefficient.

[0004] Third, there is a lack of cost-benefit comparison tools for multi-dimensional optimization directions. When the performance of a certain screen solution does not fully meet the standards, engineers face a variety of optimization options (such as increasing the thickness of the back panel, increasing the density of the sound-absorbing material, changing the type of material, etc.), but there is a lack of systematic tools to compare the "performance improvement / cost increment" ratio of each optimization direction, making it difficult to make the optimal decision.

[0005] Fourth, cost estimation and acoustic performance prediction are disconnected. In the traditional workflow, acoustic design and cost accounting belong to different departments or different stages. After the acoustic engineer determines the solution, it is then handed over to the cost department for calculation. If the cost exceeds the budget, it needs to be iterated repeatedly, which is time-consuming and inefficient.

[0006] Therefore, there is an urgent need for a reverse optimization selection method for sound barriers that can automatically search for the lowest cost screen configuration based on the target acoustic performance and provide cost comparisons in multiple dimensions. Summary of the Invention

[0007] The purpose of this application is to provide a method, device, equipment, medium and product for selecting and optimizing acoustic shields, which can automatically search for the lowest cost shield configuration based on the target acoustic performance and provide optimization direction for the lowest cost shield.

[0008] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for optimizing the selection of acoustic shielding bodies, including: Obtain the target acoustic performance requirements and constraints; Based on the target acoustic performance requirements and the constraints, all screen records in the screen measurement database are filtered to obtain a filtering scheme; The total material cost per unit area is calculated using a cost parameter library for the selected schemes, and the schemes are sorted according to the total material cost per unit area to obtain a set of recommended schemes with low cost. For the recommended scheme, calculate the performance increment and cost increment for each optimization path based on the optimization direction; Calculate the performance-cost ratio based on the performance and cost increments of each optimization path; The optimal optimization path is determined based on the performance-cost ratio, and the optimal screen selection is determined based on the optimal optimization path.

[0009] In one embodiment, each screen record in the screen measurement database includes the screen's material composition parameters and measured acoustic performance indicators; the material composition parameters include panel material, panel thickness, back panel material, back panel thickness, sound-absorbing material type, sound-absorbing material density, sound-absorbing material thickness, and accessory configuration; the measured acoustic performance indicators include measured weighted sound insulation and measured noise reduction coefficient.

[0010] In one embodiment, a screening scheme is obtained by filtering all screen records in the screen measurement database according to the target acoustic performance requirements and the constraints, specifically including: Based on the target acoustic performance requirements and the constraints, all screen records in the screen measurement database are screened to obtain a preliminary screening scheme; Candidate schemes are generated by expanding the parameter combination rules on the screen records in the screen measurement database; The candidate acoustic performance indicators were estimated using the differential areal density reduction prediction method. The candidate schemes are screened based on the target acoustic performance indicators and candidate acoustic performance requirements to obtain a second-screening scheme; A screening scheme is generated based on the initial screening scheme and the secondary screening scheme.

[0011] In one embodiment, the parameter combination expansion rules include replacing the panel material, increasing or decreasing the back panel thickness, and adjusting the density of the sound-absorbing material.

[0012] In one embodiment, the formula for the total material cost per unit area is: C_total = C_panel + C_backplate + C_sound-absorbing material + C_accessories; C_panel = ρ_panel × t_panel × P_panel; C_backplate = ρ_backplate × t_backplate × P_backplate; C_sound-absorbing material = t_sound-absorbing × P_sound-absorbing; C_Attachment = Σ(Unit price of each attachment × Quantity); Wherein, C_total is the total cost of materials per unit area, C_panel is the panel cost, C_backplate is the backplate cost, C_sound-absorbing material is the sound-absorbing material cost, C_accessories is the accessory cost, ρ_panel is the panel density, t_panel is the panel thickness, P_panel is the panel unit price, ρ_backplate is the backplate density, t_backplate is the backplate thickness, P_backplate is the backplate unit price, t_sound-absorbing is the sound-absorbing material thickness, and P_sound-absorbing is the sound-absorbing material unit price.

[0013] In one embodiment, the optimization directions include: upgrading the back panel thickness, upgrading the back panel material, increasing the density of the sound-absorbing material, increasing the thickness of the sound-absorbing material, adding cement board, and changing the type of sound-absorbing material.

[0014] Secondly, this application provides a sound shield selection optimization device, comprising: The acquisition module is used to acquire the target acoustic performance requirements and constraints; The filtering module is used to filter all screen records in the screen measurement database according to the target acoustic performance requirements and the constraints to obtain a filtering scheme; The sorting module is used to calculate the total material cost per unit area of ​​the selected schemes using a cost parameter library and sort them according to the total material cost per unit area to obtain a set of recommended schemes with low cost. The incremental calculation module is used to calculate the performance increment and cost increment of each optimization path for the recommended scheme based on the optimization direction. The performance-cost ratio calculation module is used to calculate the performance-cost ratio based on the performance increment and cost increment of each optimization path. The selection module is used to determine the optimal optimization path based on the performance-cost ratio and to determine the optimal screen selection based on the optimal optimization path.

[0015] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the sound shield selection optimization method.

[0016] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for optimizing the selection of sound shields.

[0017] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned sound shield selection optimization method.

[0018] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method, apparatus, equipment, medium, and product for optimizing the selection of acoustic shields. Based on the target acoustic performance requirements and constraints, all shield records in the measured shield database are screened, and the total material cost per unit area is calculated using a cost parameter library. Finally, optimization directions are provided to optimize the recommended scheme and obtain the optimal shield selection. This achieves automatic searching for the lowest cost shield configuration based on the target acoustic performance. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is an application environment diagram of an acoustic shield selection optimization method according to an embodiment of this application; Figure 2 A flowchart illustrating a method for optimizing the selection of acoustic shielding bodies according to an embodiment of this application; Figure 3 A functional module diagram of an acoustic shield selection optimization device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] 1. Backward optimization: In contrast to traditional forward design (which determines the solution first and then verifies the performance), backward optimization starts from the target performance and automatically searches for the optimal solution that meets the conditions.

[0023] 2. Performance-cost ratio: The performance improvement obtained per unit cost increment. The formula is (performance increment) / (cost increment). The larger the value, the higher the cost-effectiveness of the optimization direction.

[0024] 3. Parameter combination expansion: Based on existing measured data, new candidate screen configuration schemes are generated by systematically replacing or adjusting a certain parameter (such as material, thickness, density).

[0025] 4. Weighted sound insulation (Rw): A single numerical evaluation index of the sound insulation performance of the sound barrier screen, measured according to GB / T 19889.3 standard, with the unit being dB.

[0026] 5. Noise Reduction Coefficient (NRC): The arithmetic mean of the sound absorption coefficients at four frequencies: 250Hz, 500Hz, 1000Hz, and 2000Hz, used to evaluate sound absorption performance.

[0027] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] The sound shield selection optimization method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be set up independently, integrated into server 102, or placed in the cloud or on another server. Terminal 101 can send the target acoustic performance requirements and constraints to server 102. Upon receiving the target acoustic performance requirements and constraints, server 102 filters all screen records in the screen measurement database according to the target acoustic performance requirements and constraints to obtain a filtering scheme. For the filtering scheme, it calculates the total material cost per unit area using a cost parameter library and sorts them according to the total material cost per unit area to obtain a recommended scheme with low cost. For the recommended scheme, it calculates the performance increment and cost increment of each optimization path based on the optimization direction. It calculates the performance-cost ratio based on the performance increment and cost increment of each optimization path. Based on the performance-cost ratio, it determines the optimal optimization path and the optimal screen selection. Server 102 can feed back the obtained optimal screen selection to terminal 101. In addition, in some embodiments, the sound shield selection optimization can also be implemented by the server 102 or the terminal 101 alone. For example, the terminal 101 can directly optimize the sound shield selection based on the target acoustic performance requirements and constraints to be processed, or the server 102 can optimize the sound shield selection from the data storage system.

[0029] The terminal 101 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 102 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0030] In one exemplary embodiment, such as Figure 2 As shown, a method for optimizing the selection of sound shielding structures is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 102 as an example, the following steps are included.

[0031] Step 201: Obtain the target acoustic performance requirements and constraints.

[0032] Step 202: Based on the target acoustic performance requirements and the constraints, filter all screen records in the screen measurement database to obtain a filtering scheme.

[0033] Step 203: Calculate the total material cost per unit area for the selected schemes using the cost parameter library, and sort them according to the total material cost per unit area to obtain a set of recommended schemes with low cost.

[0034] Step 204: Calculate the performance increment and cost increment of each optimization path for the recommended scheme based on the optimization direction.

[0035] Step 205: Calculate the performance-cost ratio based on the performance increment and cost increment of each optimization path.

[0036] Step 206: Determine the optimal optimization path based on the performance-cost ratio and determine the optimal screen selection based on the optimal optimization path.

[0037] In an exemplary embodiment, each screen record in the screen measurement database includes the screen's material composition parameters and measured acoustic performance indicators; the material composition parameters include panel material, panel thickness, back panel material, back panel thickness, sound-absorbing material type, sound-absorbing material density, sound-absorbing material thickness, and accessory configuration; the measured acoustic performance indicators include measured weighted sound insulation and measured noise reduction coefficient.

[0038] In practical applications, it is first necessary to build a screen measurement database and a cost parameter database.

[0039] A database of measured sound barrier panels was established, containing multiple measured records for each panel. Each record includes the panel's material composition parameters (panel material and thickness, back panel material and thickness, sound-absorbing material type, density and thickness, and accessory configuration) and corresponding measured acoustic indicators (weighted sound insulation Rw, noise reduction coefficient NRC). The database supports dynamic expansion and can continuously import new test report data.

[0040] Establish a cost parameter library that can be customized by users, specifically the material unit price parameter library shown in Table 1.

[0041] Table 1 Material Unit Price Parameter Library

[0042] In an exemplary embodiment, the target acoustic performance requirements and constraints are obtained, specifically by receiving the target acoustic performance requirements set by the user, including: target noise reduction coefficient NRC (e.g., ≥0.85) and target weighted sound insulation Rw (e.g., ≥30dB). The user can also set constraints, such as limiting the range of panel materials, limiting the upper limit of the total thickness of the screen, and limiting the screen type (general-purpose for highways / dedicated for railways), etc.

[0043] In an exemplary embodiment, a screening scheme is obtained by filtering all screen records in the screen measurement database according to the target acoustic performance requirements and the constraints. Specifically, this includes: filtering all screen records in the screen measurement database according to the target acoustic performance requirements and the constraints to obtain a preliminary screening scheme; generating candidate schemes from the screen records in the screen measurement database using parameter combination expansion rules; estimating candidate acoustic performance indicators for the candidate schemes using a differentiated areal density reduction prediction method; filtering the candidate schemes according to the target acoustic performance indicators and candidate acoustic performance requirements to obtain a secondary screening scheme; and generating a final screening scheme based on the preliminary screening scheme and the secondary screening scheme. By traversing all screen records in the measurement database, schemes that simultaneously satisfy the following conditions are selected: measured NRC ≥ target NRC, and measured Rw ≥ target Rw, and user-defined constraints are also met.

[0044] In one exemplary embodiment, the parameter combination expansion rules include replacing the panel material, increasing or decreasing the back panel thickness, and adjusting the density of the sound-absorbing material.

[0045] In practical applications, parameter combination expansion specifically involves generating more candidate solutions based on existing records in the database. Expansion rules include: replacing existing panel materials with other optional materials (e.g., replacing aluminum with galvanized steel); increasing or decreasing the existing back panel thickness by one level (e.g., adjusting from 0.8mm to 1.0mm or 0.6mm); and adjusting the existing sound-absorbing material density by one level (e.g., from 48kg / m³). 3 Adjusted to 80kg / m 3 For the extended candidate schemes, the Rw and NRC are estimated using the differentiated areal density reduction prediction method, and schemes that meet the target performance are selected.

[0046] In an exemplary embodiment, for each screen design that meets the conditions, the total material cost per unit area is calculated based on the material unit price in the cost parameter library. All designs that meet the conditions are sorted in ascending order of total cost per unit area, and the top N designs with the lowest costs are recommended.

[0047] The formula for the total cost of materials per unit area is: C_total = C_panel + C_backplate + C_sound-absorbing material + C_accessories.

[0048] C_panel = ρ_panel × t_panel × P_panel.

[0049] C_backplate = ρ_backplate × t_backplate × P_backplate.

[0050] C_sound-absorbing material = t_sound-absorbing × P_sound-absorbing.

[0051] C_Attachment = Σ (Unit price of each attachment × Quantity used).

[0052] Wherein, C_total is the total cost of materials per unit area, C_panel is the panel cost, C_backplate is the backplate cost, C_sound-absorbing material is the sound-absorbing material cost, C_accessories is the accessory cost, ρ_panel is the panel density, t_panel is the panel thickness, P_panel is the panel unit price, ρ_backplate is the backplate density, t_backplate is the backplate thickness, P_backplate is the backplate unit price, t_sound-absorbing is the sound-absorbing material thickness, and P_sound-absorbing is the sound-absorbing material unit price.

[0053] In one exemplary embodiment, the optimization directions include: upgrading the back panel thickness, upgrading the back panel material, increasing the density of the sound-absorbing material, increasing the thickness of the sound-absorbing material, adding cement board, and changing the type of sound-absorbing material.

[0054] In practical applications, for each recommended solution, multiple optimization directions are automatically generated, and the performance and cost increments of each optimization path are calculated to form a cost comparison table. The optimization directions are shown in Table 2.

[0055] Table 2 Optimization Direction Table

[0056] For each optimization direction, calculate the performance-cost ratio (performance improvement / cost increase), sort by performance-cost ratio in descending order, and recommend the optimization path that "gets the greatest performance improvement with the least amount of money".

[0057] This application allows users to input acoustic performance requirements (NRC≥X, Rw≥Y), setting constraints (material / thickness / category), where X and Y are set values. The database is then traversed to filter all records, selecting those with NRC≥X and Rw≥Y. Next, parameter combinations are expanded, including material replacement / thickness / density adjustment, to predict Rw and NRC and filter solutions that meet the conditions. Cost estimation is then performed, calculating the unit area cost for each solution and sorting them in ascending order of cost. Finally, multi-dimensional optimization comparisons are conducted to generate optimization directions for each solution, calculating performance increments / cost increments and sorting them by performance-cost ratio. The final output includes: a list of optimal solutions + an optimization comparison table for each solution.

[0058] First, this application provides a reverse optimization method that derives the lowest-cost configuration from the target performance. Unlike traditional forward trial calculations (selecting a configuration first and then verifying performance), this application starts from the target NRC and Rw, automatically traverses the database and parameter combination extension space, searches for all solutions that meet the conditions, and sorts them by cost, thus achieving a reverse mapping from performance to cost.

[0059] Second, this application provides a cost-benefit comparison method for multi-dimensional optimization directions. For each recommended solution, multiple optimization directions are automatically generated (upgrading the backplate, increasing density, increasing thickness, adding accessories, changing materials), and the performance increment / cost increment ratio for each direction is calculated, providing engineers with a quantitative basis for optimization decisions.

[0060] Third, this application integrates acoustic performance prediction and cost estimation into a unified optimization. By integrating the acoustic performance prediction model and the cost estimation model into the same system, real-time linkage calculation of performance and cost is achieved, eliminating the problem of separation between acoustic design and cost accounting in the traditional workflow.

[0061] This application has the following effects: First, it significantly improves the efficiency of sound barrier selection. Traditional forward trial-and-error methods require engineers to try different configurations one by one and manually calculate costs, typically taking hours or even days to complete the selection for a project. Using the reverse optimization method proposed in this application, the system can traverse all feasible solutions and output the optimal recommendation within seconds, improving selection efficiency by tens of times. This effect stems from the global search and automatic sorting mechanism.

[0062] Secondly, it avoids the blind spots of experience in manual selection. When engineers select based on experience, they are often limited to a few "common solutions" and may miss more cost-effective unconventional combinations. This application ensures that no potential optimal solution is overlooked by systematically traversing all feasible combinations (including new solutions generated by parameter combination extensions).

[0063] Third, it provides a quantitative basis for optimization decisions. The cost-benefit ratio selection table for multi-dimensional optimization directions allows engineers to clearly see the input-output ratio of each optimization path, avoiding the blindness of making decisions based on intuition. For example, when the sound insulation does not meet the standard, engineers can intuitively compare the additional cost of increasing the back panel thickness by 0.2mm to X yuan / m. 2 This can increase Rw by approximately 2dB, while adding cement board requires an additional cost of Y yuan / m. 2 This can improve Rw by about 4dB, thus allowing you to choose the most cost-effective optimization path.

[0064] Fourth, it eliminates the iterative cycle of acoustic design and cost accounting. The integrated performance-cost optimization allows acoustic performance and material costs to be displayed in real time on the same interface. Engineers can see the changes in performance and cost simultaneously when adjusting any parameter, eliminating the need for repeated communication between the acoustics and cost departments and significantly shortening the project decision-making cycle.

[0065] Based on the same inventive concept, this application also provides an acoustic shield selection optimization device for implementing the acoustic shield selection optimization method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more acoustic shield selection optimization device embodiments provided below can be found in the limitations of the acoustic shield selection optimization method described above, and will not be repeated here.

[0066] In one exemplary embodiment, such as Figure 3 As shown, a sound shield selection optimization device is provided, comprising: The acquisition module is used to acquire the target acoustic performance requirements and constraints.

[0067] The filtering module is used to filter all screen records in the screen measurement database according to the target acoustic performance requirements and the constraints to obtain a filtering scheme.

[0068] The sorting module is used to calculate the total material cost per unit area using a cost parameter library and sort the selected schemes according to the total material cost per unit area to obtain a set of recommended schemes with low cost.

[0069] The incremental calculation module is used to calculate the performance increment and cost increment of each optimization path for the recommended scheme based on the optimization direction.

[0070] The performance-cost ratio calculation module is used to calculate the performance-cost ratio based on the performance increment and cost increment of each optimization path.

[0071] The selection module is used to determine the optimal optimization path based on the performance-cost ratio and to determine the optimal screen selection based on the optimal optimization path.

[0072] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores sound shield selection optimization data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a sound shield selection optimization method.

[0073] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment to which the present application is applied. Specific computer equipment may include, for example, [the following is a list of possible additional structures]. Figure 4 The embodiments show more or fewer components, combinations of certain components, or different component arrangements. In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the above-described method embodiments.

[0074] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method embodiments.

[0075] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method embodiments.

[0076] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0077] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.

[0078] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0079] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0080] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for optimizing the selection of sound shielding bodies, characterized in that, include: Obtain the target acoustic performance requirements and constraints; Based on the target acoustic performance requirements and the constraints, all screen records in the screen measurement database are filtered to obtain a filtering scheme; The total material cost per unit area is calculated using a cost parameter library for the selected schemes, and the schemes are sorted according to the total material cost per unit area to obtain a set of recommended schemes with low cost. For the recommended scheme, calculate the performance increment and cost increment for each optimization path based on the optimization direction; Calculate the performance-cost ratio based on the performance and cost increments of each optimization path; The optimal optimization path is determined based on the performance-cost ratio, and the optimal screen selection is determined based on the optimal optimization path.

2. The method for optimizing the selection of acoustic shielding bodies according to claim 1, characterized in that, Each screen record in the screen measurement database includes the screen's material composition parameters and measured acoustic performance indicators; the material composition parameters include panel material, panel thickness, back panel material, back panel thickness, sound-absorbing material type, sound-absorbing material density, sound-absorbing material thickness, and accessory configuration; the measured acoustic performance indicators include measured weighted sound insulation and measured noise reduction coefficient.

3. The method for optimizing the selection of acoustic shielding bodies according to claim 1, characterized in that, Based on the target acoustic performance requirements and the constraints, all screen records in the screen measurement database are filtered to obtain a filtering scheme, which specifically includes: Based on the target acoustic performance requirements and the constraints, all screen records in the screen measurement database are screened to obtain a preliminary screening scheme; Candidate schemes are generated by expanding the parameter combination rules on the screen records in the screen measurement database; The candidate acoustic performance indicators were estimated using the differential areal density reduction prediction method. The candidate schemes are screened based on the target acoustic performance indicators and candidate acoustic performance requirements to obtain a second-screening scheme; A screening scheme is generated based on the initial screening scheme and the secondary screening scheme.

4. The method for optimizing the selection of sound shielding bodies according to claim 3, characterized in that, The parameter combination expansion rules include replacing the panel material, increasing or decreasing the back panel thickness, and adjusting the density of the sound-absorbing material.

5. The method for optimizing the selection of acoustic shielding bodies according to claim 1, characterized in that, The formula for the total cost of materials per unit area is: C_total = C_panel + C_backplate + C_sound-absorbing material + C_accessories; C_panel = ρ_panel × t_panel × P_panel; C_backplate = ρ_backplate × t_backplate × P_backplate; C_sound-absorbing material = t_sound-absorbing × P_sound-absorbing; C_Attachment = Σ(Unit price of each attachment × Quantity); Wherein, C_total is the total cost of materials per unit area, C_panel is the panel cost, C_backplate is the backplate cost, C_sound-absorbing material is the sound-absorbing material cost, C_accessories is the accessory cost, ρ_panel is the panel density, t_panel is the panel thickness, P_panel is the panel unit price, ρ_backplate is the backplate density, t_backplate is the backplate thickness, P_backplate is the backplate unit price, t_sound-absorbing is the sound-absorbing material thickness, and P_sound-absorbing is the sound-absorbing material unit price.

6. The method for optimizing the selection of acoustic shielding bodies according to claim 1, characterized in that, The optimization directions include: upgrading the back panel thickness, upgrading the back panel material, increasing the density of the sound-absorbing material, increasing the thickness of the sound-absorbing material, adding cement board, and changing the type of sound-absorbing material.

7. A sound shield selection optimization device, characterized in that, include: The acquisition module is used to acquire the target acoustic performance requirements and constraints; The filtering module is used to filter all screen records in the screen measurement database according to the target acoustic performance requirements and the constraints to obtain a filtering scheme; The sorting module is used to calculate the total material cost per unit area of ​​the selected schemes using a cost parameter library and sort them according to the total material cost per unit area to obtain a set of recommended schemes with low cost. The incremental calculation module is used to calculate the performance increment and cost increment of each optimization path for the recommended scheme based on the optimization direction. The performance-cost ratio calculation module is used to calculate the performance-cost ratio based on the performance increment and cost increment of each optimization path. The selection module is used to determine the optimal optimization path based on the performance-cost ratio and to determine the optimal screen selection based on the optimal optimization path.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the sound shield selection optimization method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the sound shield selection optimization method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the sound shield selection optimization method as described in any one of claims 1-6.

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