Building sound insulation optimization design system and method fused with sound quality evaluation

Abstract

The invention discloses a building sound insulation optimization design system and method fused with sound quality evaluation. The system comprises a data acquisition module, a noise analysis module, a sound quality analysis module, a sound insulation demand determination module, a sound insulation scheme intelligent recommendation module, an audible processing module, a user feedback module, a secondary optimization module and a report generation and data storage module. All the modules are in data connection and work cooperatively. The system obtains indoor noise data, analyzes noise features and psychoacoustic parameters, intelligently screens and recommends a sound insulation scheme, carries out audible processing for users to listen, carries out iterative optimization according to user feedback, and finally generates a sound insulation design report. Acoustic analysis of the system focuses on subjective feelings of users, accuracy of sound insulation design and user satisfaction are improved, and intelligentization of building sound insulation design of the urban environment is promoted.

Description

Technical Field

[0001] The present invention relates to the field of architectural acoustics and intelligent optimization technology, and in particular to a building sound insulation optimization design system and method integrating sound quality evaluation. Background Art

[0002] With the acceleration of urbanization and the improvement of people's living standards, the problem of noise pollution in the building environment is becoming increasingly serious. The traditional building sound insulation design method mainly relies on experience and simple calculations, which is difficult to meet the user's personalized needs for sound insulation effects in a timely and accurate manner. At the same time, the existing technology lacks a comprehensive analysis of indoor noise, especially the consideration of noise sound quality parameters and user subjective feelings, resulting in a gap between the sound insulation design effect and user expectations. Therefore, there is an urgent need for an intelligent sound insulation design system that can comprehensively consider indoor noise characteristics, sound quality parameters, user feedback and other factors. Summary of the invention

[0003] The purpose of the present invention is to provide a building sound insulation optimization design system and method integrating sound quality evaluation, which can be used for intelligent evaluation and optimization of building sound insulation and improvement of user subjective satisfaction.

[0004] The technical solution adopted by the present invention is: A building sound insulation optimization design system integrating sound quality evaluation, including the following modules: Data acquisition module: obtain building area environmental data and indoor and outdoor noise data; Noise analysis module: connected to the data acquisition module, performs feature analysis on indoor noise data to determine whether it meets the noise standards and provides detailed information; Sound quality analysis module: connected to the noise analysis module, evaluates the psychoacoustic impact of noise on users and provides psychoacoustic parameters, including loudness, annoyance, sharpness, roughness, volatility, and language interference level; The sound insulation demand determination module is connected to the sound quality analysis module to determine the user's sound insulation demand based on the noise analysis and sound quality analysis results; Sound insulation scheme intelligent recommendation module: connects to the sound insulation demand determination module, loads the combined wall sound insulation database, performs intelligent screening based on the sound insulation demand analysis results, and outputs multiple sound insulation schemes; Auralization processing module: connected to the intelligent sound insulation scheme recommendation module, performs auralization processing on the sound insulation scheme selected by the user, and generates audible sample sounds for the user to audition; User feedback module: connected to the auralization processing module to obtain the user's subjective evaluation of the sound insulation solution; Secondary optimization module: connected to the user feedback module, adjusts the noise spectrum according to user feedback, and optimizes the sound insulation scheme based on the adjusted noise spectrum; Report generation and data saving module: connected to the secondary optimization module, generates a sound insulation design report based on the adjusted design plan that meets user needs, and saves design data and user feedback information.

[0005] Furthermore, the data acquisition module supports a noise map big data model interface for obtaining regional noise information.

[0006] Furthermore, the sonification processing module includes: Audio processing unit: uses digital signal processing technology to simulate the noise attenuation effect of the sound insulation structure, filters and processes the original noise signal, and generates audible sample sound; Audition unit: provides users with an interface for auditioning the effects of different sound insulation solutions, supports functions such as volume adjustment and playback control, and enhances user experience.

[0007] Furthermore, the secondary optimization module identifies the problem noise spectrum, analyzes the specific frequency band that needs to be processed based on the user's subjective opinion to make noise reduction adjustments, and reselects the sound insulation solution that is closest to the sound insulation effect from the partition wall database.

[0008] Furthermore, the report generation and data preservation module is used to save user feedback information to the system database for deep learning and algorithm improvement of the system.

[0009] Furthermore, the sound insulation design report generated by the report generation and data saving module includes: Project details: structural information, material specifications, and construction requirements of the sound insulation structure; Acoustic parameters: sound insulation performance indicators, such as sound insulation and spectrum characteristics; Psychoacoustic parameters: loudness, annoyance, sharpness, roughness, volatility, speech interference level; Effect prediction: prediction and analysis of indoor noise level after sound insulation; User feedback summary: users’ subjective evaluation and satisfaction with the solution.

[0010] A building sound insulation optimization method integrating sound quality evaluation, the specific steps are as follows: Step 1 (201) Data collection: Obtain building area environmental data and indoor and outdoor noise data, support multiple data acquisition methods, and ensure the accuracy and comprehensiveness of the data.

[0011] Step 2 (202) Noise analysis: Perform feature analysis on the collected noise data, including sound pressure level, spectrum characteristics, etc., to determine whether it meets the corresponding noise standards and generate a detailed analysis report.

[0012] Step 3 (203) Sound quality analysis: Evaluate the psychoacoustic impact of noise on users, calculate psychoacoustic parameters, quantify the impact of noise on users' subjective feelings, and provide a basis for determining sound insulation requirements.

[0013] Step 4 (204) Determine the sound insulation requirement: Determine specific sound insulation requirement parameters based on the noise analysis and sound quality analysis results and user needs.

[0014] Step 5 (205) Intelligent recommendation of sound insulation schemes: Load the combined wall sound insulation database, use intelligent algorithms, filter according to the sound insulation demand analysis results, and output multiple sound insulation schemes that meet the requirements.

[0015] Step 6 (206) Auralization processing: Auralization processing is performed on the sound insulation scheme selected by the user to generate audible sample sounds for the user to intuitively experience the effects of different sound insulation schemes.

[0016] Step 7 (207) User feedback acquisition: Provide a user audition interface to obtain the user's subjective evaluation and feedback information on the sound insulation solution.

[0017] Step 8 (208) Secondary optimization: If the user is not satisfied with the current solution, the problematic noise spectrum is identified based on user feedback, and noise reduction adjustments are made. The adjusted noise spectrum is used to optimize the sound insulation solution, and the closest sound insulation solution is reselected from the partition wall database to enter a new iterative process.

[0018] Step 9 (209) Report generation and data preservation: If the user confirms a satisfactory solution, a complete sound insulation design report is generated, including design solution details, acoustic parameters, psychoacoustic parameters and noise control effect prediction. At the same time, the design data and user feedback information are saved for deep learning and algorithm improvement of the system.

[0019] Furthermore, the secondary optimization in step 8 specifically includes the following steps: Step 8-1, user feedback analysis (401): Based on the information provided by the user, identify the noise frequency bands and feelings that the user is subjectively dissatisfied with (for example: harshness: feeling that high-frequency sounds are too sharp or harsh; low-frequency roar: feeling that low-frequency sounds are too dull or roaring; lack of clarity: the sound is fuzzy, not clear enough, and lacks mid-frequency details; noise is too monotonous or fluctuating: feeling that the noise is too monotonous or has obvious fluctuations and roughness; discomfort in a specific frequency band: pointing out that the sound in a certain frequency range makes people uncomfortable).

[0020] Step 8-2, problem spectrum identification (402): using spectrum analysis technology, identify the problem noise spectrum and determine the frequency range that needs to be adjusted.

[0021] Step 8-3, noise reduction adjustment (403): performing targeted noise reduction processing on the identified problematic frequency band to adjust the noise spectrum.

[0022] Step 8-4, solution optimization (404): Based on the adjusted noise spectrum, the closest sound insulation solution is reselected from the partition wall database to optimize the sound insulation design.

[0023] Step 8-5, sonification processing (405): the new sound insulation solution is iterated again and sonification processing is performed for user audition and feedback until the user is satisfied.

[0024] The present invention adopts the above technical scheme, and the beneficial effects compared with the prior art are as follows: by introducing sound quality analysis and auralization processing, the indoor and outdoor noise characteristics and psychoacoustic parameters are comprehensively analyzed, the sound insulation scheme is intelligently recommended, and secondary optimization is performed according to user feedback, thereby improving the accuracy of the sound insulation design and user satisfaction, and having significant technical progress and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments; Figure 1 A schematic diagram of the structure of a building sound insulation optimization design system integrating sound quality evaluation according to the present invention; Figure 2 A schematic diagram of the workflow of a building sound insulation optimization design system integrating sound quality evaluation according to the present invention; Figure 3 is a functional schematic diagram of the sonification processing module of the present invention; Figure 4 It is a schematic diagram of the flow of the secondary optimization module of the present invention; Figure 5 Schematic diagram of a sound insulation design report generated for the system of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0027] like Figures 1 to 5 As shown in one, the present invention discloses a building sound insulation optimization design system integrating sound quality evaluation, including the following modules: The data acquisition module (101) is used to obtain building area environmental data and indoor and outdoor noise data, and supports a noise map big data model interface to facilitate the acquisition of regional noise information.

[0028] The noise analysis module (102) is connected to the module 1, performs characteristic analysis on the indoor noise data, including sound pressure level, spectrum characteristics, etc., determines whether it meets the noise standard, and provides a detailed analysis report.

[0029] Specifically, the present invention can obtain building area environmental data, and use the noise analysis module to perform characteristic analysis on indoor noise to determine whether the noise meets the corresponding standards. This refined analysis method significantly improves the accuracy of sound insulation design and makes the design scheme more in line with actual environmental requirements.

[0030] The sound quality analysis module (103) is connected to the module 2, evaluates the psychoacoustic impact of noise on the user, calculates psychoacoustic parameters, including loudness, annoyance, sharpness, roughness, fluctuation and language interference level, and quantifies the impact of noise on the user's subjective feelings.

[0031] Specifically, the present invention uses a sound quality analysis module to evaluate the psychoacoustic impact of noise on users and quantify psychoacoustic parameters such as loudness, annoyance, and sharpness. This analysis focuses on the user's subjective feelings, so that the sound insulation design not only focuses on physical performance parameters, but is closer to the actual needs of users, thereby improving the user's sound insulation satisfaction.

[0032] The sound insulation requirement determination module (104) is connected to the module 3 and determines specific sound insulation requirement parameters according to the noise analysis and sound quality analysis results combined with user needs.

[0033] The sound insulation scheme intelligent recommendation module (105) is connected to module 4, loads the combined wall sound insulation database, uses an intelligent algorithm, screens according to the sound insulation demand analysis results, outputs multiple sound insulation schemes that meet the requirements, and lists detailed information such as their structural information, sound insulation performance, and psychoacoustic parameters.

[0034] Specifically, the system determines the results based on the sound quality analysis results and sound insulation requirements, and uses the sound insulation solution intelligent recommendation module and the sound insulation database to screen out a variety of sound insulation solutions to provide users with a wealth of choices. The intelligent screening algorithm improves the efficiency and accuracy of the recommended solutions, ensuring that the solutions better meet the personalized needs of users.

[0035] The auralization processing module (106), connected to the module 5, comprises an audio processing unit, which simulates the noise attenuation effect of the sound insulation structure in the actual environment, performs auralization processing on the sound insulation scheme selected by the user, and generates audible sample sounds.

[0036] Specifically, the sonification processing module in the present invention can simulate the sound insulation scheme selected by the user and generate audible sound samples, so that the user can evaluate the sound insulation effect through intuitive auditory experience. The sonification function improves the user's understanding and participation in the sound insulation scheme, and provides users with a more personalized and interactive design experience.

[0037] The user feedback module (107) is connected to the module 6, provides a user audition interface, and obtains the user's subjective evaluation and feedback information on the sound insulation solution.

[0038] The secondary optimization module (108) is connected to the module 7, and identifies the problem noise spectrum according to the user feedback, and obtains the specific frequency band to be processed according to the user's subjective opinion analysis to perform noise reduction adjustment. The adjusted noise spectrum is used to optimize the sound insulation scheme, and the sound insulation scheme closest to the sound insulation effect is reselected from the partition wall database.

[0039] Specifically, the user feedback module and the secondary optimization module of the present invention form a closed-loop mechanism for user feedback. The system identifies the problem noise spectrum based on the user's subjective evaluation of the sound insulation solution, and makes targeted noise reduction adjustments to the solution, so that the final solution better meets the user's subjective needs. The secondary optimization function enhances the flexibility and adaptability of the system and significantly improves user satisfaction.

[0040] The report generation and data storage module (109) is connected to the module 8, generates a sound insulation design report according to the final design solution that meets the user's needs, and stores the design data and user feedback information for deep learning and algorithm improvement of the system.

[0041] Specifically, the report generation and data storage module not only generates sound insulation design reports, but also stores user feedback data and design data into the system database for subsequent deep learning and algorithm improvement. This function enables the system to have self-learning capabilities, continuously optimize sound insulation design strategies, and improve the level of intelligence and the rationality of design solutions.

[0042] See also Figure 2 The present invention also discloses a building sound insulation optimization method integrating sound quality evaluation. System initialization: system startup, parameter setting, loading of relevant standards and configuration files, and ensuring the correctness of the analysis process; the specific steps are as follows: Step 1 (201) Data collection: Obtain building area environmental data and indoor and outdoor noise data, support multiple data acquisition methods, and ensure the accuracy and comprehensiveness of the data.

[0043] Step 2 (202) Noise analysis: Perform feature analysis on the collected noise data, including sound pressure level, spectrum characteristics, etc., to determine whether it meets the corresponding noise standards and generate a detailed analysis report.

[0044] Step 3 (203) Sound quality analysis: Evaluate the psychoacoustic impact of noise on users, calculate psychoacoustic parameters, quantify the impact of noise on users' subjective feelings, and provide a basis for determining sound insulation requirements.

[0045] Step 4 (204) Determine the sound insulation requirement: Determine specific sound insulation requirement parameters based on the noise analysis and sound quality analysis results and user needs.

[0046] Step 5 (205) Intelligent recommendation of sound insulation schemes: Load the combined wall sound insulation database, use intelligent algorithms, filter according to the sound insulation demand analysis results, and output multiple sound insulation schemes that meet the requirements.

[0047] Step 6 (206) Auralization: Auralization is performed on the sound insulation solution selected by the user. Figure 3 , generate audible sample sounds for users to intuitively experience the effects of different sound insulation solutions.

[0048] Step 7 (207) User feedback acquisition: Provide a user audition interface to obtain the user's subjective evaluation and feedback information on the sound insulation solution.

[0049] Step 8 (208) Secondary optimization: If the user is not satisfied with the current solution, identify the problematic noise spectrum based on user feedback and make noise reduction adjustments. The adjusted noise spectrum is used to optimize the sound insulation solution and reselect the closest sound insulation solution from the partition wall database. Figure 4 , entering a new iterative process.

[0050] Step 9 (209) Report generation and data preservation: If the user confirms a satisfactory solution, a complete sound insulation design report is generated, including design solution details, acoustic parameters, psychoacoustic parameters and noise control effect prediction, see Figure 5 At the same time, design data and user feedback information are saved for deep learning and algorithm improvement of the system.

[0051] Furthermore, if Figure 3 As shown, the auditory processing module includes: The audio processing unit (301) uses digital signal processing technology to simulate the noise attenuation effect of the sound insulation structure, filter and process the original noise signal, and generate audible sample sound.

[0052] Audition unit (302): provides an interface for users to audition the effects of different sound insulation solutions, supports functions such as volume adjustment and playback control, and enhances user experience.

[0053] Furthermore, if Figure 4 As shown, the workflow of the secondary optimization module is: User feedback analysis (401): Based on user feedback information, identify the noise frequency bands and feelings that users are subjectively dissatisfied with (for example: harshness: feeling that high-frequency sounds are too sharp or harsh; low-frequency roar: feeling that low-frequency sounds are too dull or roaring; lack of clarity: the sound is fuzzy, not clear enough, and lacks mid-frequency details; noise is too monotonous or fluctuating: feeling that the noise is too monotonous or has obvious fluctuations and roughness; discomfort in specific frequency bands: pointing out that the sound in a certain frequency range makes people uncomfortable).

[0054] Problem spectrum identification (402): Spectrum analysis technology is used to identify the problem noise spectrum and determine the frequency range that needs to be adjusted.

[0055] Noise reduction adjustment (403): Targeted noise reduction processing is performed on the identified problem frequency band to adjust the noise spectrum.

[0056] Solution optimization (404): Based on the adjusted noise spectrum, reselect the closest sound insulation solution from the partition wall database to optimize the sound insulation design.

[0057] Auralization (405): The new sound insulation solution is iterated again and auralized for users to listen and provide feedback until the users are satisfied.

[0058] Furthermore, if Figure 5 As shown in the figure, the sound insulation design report generated by the report generation and data saving module includes: Project details: structural information, material specifications, construction requirements, etc. of the sound insulation structure.

[0059] Acoustic parameters: sound insulation performance indicators, such as sound insulation volume, spectrum characteristics, etc.

[0060] Psychoacoustic parameters: loudness, annoyance, sharpness, roughness, volatility, speech interference level, etc.

[0061] Effect prediction: prediction and analysis of indoor noise level after sound insulation.

[0062] User feedback summary: users’ subjective evaluation and satisfaction with the solution.

[0063] At the same time, the module saves the design data and user feedback information to the system database for deep learning and algorithm improvement of the system, continuously improving the intelligence level of the system and user satisfaction.

[0064] Compared with the prior art, the present invention introduces sound quality analysis and auralization processing, comprehensively analyzes indoor and outdoor noise characteristics and psychoacoustic parameters, intelligently recommends sound insulation solutions, and performs secondary optimization based on user feedback, thereby improving the accuracy of sound insulation design and user satisfaction, and has significant technical progress and practical value. Through the automated data analysis, intelligent recommendation and auralization processing functions of the system of the present invention, the efficiency of building sound insulation design is significantly improved, making the formulation of sound insulation solutions faster and more accurate. The present invention can reduce a large amount of manual intervention in traditional sound insulation design, promote the popularization of building sound insulation design in practical applications, and has broad social value.

[0065] Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians of the art without making creative work are within the scope of protection of the present application.

Claims

1. A building sound insulation optimization design system integrating sound quality evaluation, characterized by: It includes the following modules: Data acquisition module: obtain building area environmental data and indoor and outdoor noise data; Noise analysis module: connected to the data acquisition module, performs feature analysis on indoor noise data to determine whether it meets the noise standards and provides detailed information; Sound quality analysis module: connected to the noise analysis module, evaluates the psychoacoustic impact of noise on users and provides psychoacoustic parameters, including loudness, annoyance, sharpness, roughness, volatility, and language interference level; The sound insulation demand determination module is connected to the sound quality analysis module to determine the user's sound insulation demand based on the noise analysis and sound quality analysis results; Sound insulation scheme intelligent recommendation module: connects to the sound insulation demand determination module, loads the combined wall sound insulation database, performs intelligent screening based on the sound insulation demand analysis results, and outputs multiple sound insulation schemes; Auralization processing module: connected to the intelligent sound insulation scheme recommendation module, performs auralization processing on the sound insulation scheme selected by the user, and generates audible sample sounds for the user to audition; User feedback module: connected to the auralization processing module to obtain the user's subjective evaluation of the sound insulation solution; Secondary optimization module: connected to the user feedback module, adjusts the noise spectrum according to user feedback, and optimizes the sound insulation scheme based on the adjusted noise spectrum; Report generation and data saving module: connected to the secondary optimization module, generates a sound insulation design report based on the adjusted design plan that meets user needs, and saves design data and user feedback information.

2. The building sound insulation optimization design system integrating sound quality evaluation according to claim 1 is characterized by: The data acquisition module supports the noise map big data model interface, which is used to obtain noise information in a certain area.

3. The building sound insulation optimization design system integrating sound quality evaluation according to claim 1 is characterized by: The sonification processing module includes: Audio processing unit: uses digital signal processing technology to simulate the noise attenuation effect of the sound insulation structure, filters and processes the original noise signal, and generates audible sample sound; Audition unit: provides users with an interface for auditioning the effects of different sound insulation solutions, supports functions such as volume adjustment and playback control, and enhances user experience.

4. The building sound insulation optimization design system integrating sound quality evaluation according to claim 1 is characterized by: The secondary optimization module identifies the problem noise spectrum, analyzes the specific frequency band that needs to be processed based on the user's subjective opinion to make noise reduction adjustments, and reselects the sound insulation solution that is closest to the sound insulation effect from the partition wall database.

5. The building sound insulation optimization design system integrating sound quality evaluation according to claim 1 is characterized by: The report generation and data preservation module is used to save user feedback information to the system database for deep learning and algorithm improvement of the system.

6. The building sound insulation optimization design system integrating sound quality evaluation according to claim 1 is characterized by: The sound insulation design report generated by the report generation and data saving module includes: Project details: structural information, material specifications, and construction requirements of the sound insulation structure; Acoustic parameters: sound insulation performance indicators, such as sound insulation and spectrum characteristics; Psychoacoustic parameters: loudness, annoyance, sharpness, roughness, volatility, speech interference level; Effect prediction: prediction and analysis of indoor noise level after sound insulation; User feedback summary: users’ subjective evaluation and satisfaction with the solution.

7. A building sound insulation optimization method integrating sound quality evaluation, according to the building sound insulation optimization design system integrating sound quality evaluation according to any one of claims 1 to 6, characterized in that: The method comprises the following steps: Step 1, data collection: obtain building area environmental data and indoor and outdoor noise data; Step 2, noise analysis: perform feature analysis on the collected noise data, including sound pressure level, spectrum characteristics, etc., determine whether it meets the corresponding noise standards, and generate a detailed analysis report; Step 3, sound quality analysis: evaluate the psychoacoustic impact of noise on users, calculate psychoacoustic parameters, quantify the impact of noise on users' subjective feelings, and provide a basis for determining sound insulation requirements; Step 4: Determine the sound insulation requirements: Determine the specific sound insulation requirements parameters based on the noise analysis and sound quality analysis results and user needs; Step 5: Intelligent recommendation of sound insulation solutions: Load the combined wall sound insulation database, use intelligent algorithms to filter according to the sound insulation demand analysis results, and output multiple sound insulation solutions that meet the requirements; Step 6, sonification processing: sonification processing is performed on the sound insulation scheme selected by the user to generate audible sample sounds for the user to intuitively experience the effects of different sound insulation schemes; Step 7, user feedback acquisition: provide a user audition interface to obtain the user's subjective evaluation and feedback information on the sound insulation solution; Step 8, secondary optimization: If the user is not satisfied with the current solution, identify the problematic noise spectrum based on user feedback and make noise reduction adjustments. The adjusted noise spectrum is used to optimize the sound insulation solution, and the closest sound insulation solution is reselected from the partition wall database to enter a new iterative process. Step 9, report generation and data preservation: If the user confirms a satisfactory solution, a complete sound insulation design report will be generated, including design solution details, acoustic parameters, psychoacoustic parameters and noise control effect prediction; At the same time, design data and user feedback information are saved for deep learning and algorithm improvement of the system.

8. The method for optimizing building sound insulation by integrating sound quality evaluation according to claim 7, characterized in that: The secondary optimization in step 8 specifically includes the following steps: Step 8-1, user feedback analysis: based on user feedback, identify the noise frequency bands and feelings that users are subjectively dissatisfied with; Step 8-2, problem spectrum identification: Use spectrum analysis technology to identify the problem noise spectrum and determine the frequency range that needs to be adjusted; Step 8-3, noise reduction adjustment: perform targeted noise reduction processing on the identified problem frequency band and adjust the noise spectrum; Step 8-4, solution optimization: according to the adjusted noise spectrum, reselect the closest sound insulation solution from the partition wall database to optimize the sound insulation design; Step 8-5, sonification processing: the new sound insulation scheme is iterated again and sonification processing is performed for users to audition and provide feedback until the users are satisfied.

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