College dormitory noise multi-dimensional evaluation method based on psychoacoustic and sound field simulation

Abstract

The invention discloses a college dormitory noise multi-dimensional evaluation method based on psychoacoustic and sound field simulation, and belongs to the field of building environment and noise control. The objective of the invention is to solve the problem that an existing dormitory noise evaluation method is single in index and neglects subjective perception and spatial distribution heterogeneity. The method comprises the following steps: acquiring physical data and original audio of dormitory noise through multi-point synchronous acquisition; calculating loudness, sharpness, roughness, fluctuation intensity and comprehensive psychoacoustic annoyance (PA) by using a psychoacoustic model, and scientifically quantifying subjective interference of noise; meanwhile, sound field simulation is carried out based on the accurately established three-dimensional dormitory model, a sound pressure level distribution cloud picture is generated, and a standing wave phenomenon and a sound energy accumulation area are visually revealed; and finally, generating a targeted noise reduction optimization strategy by integrating subjective and objective and spatial analysis results. According to the method, full-chain diagnosis of physical characteristics-subjective perception-spatial distribution of dormitory noise is realized, and a scientific basis is provided for accurate and efficient noise treatment.

Description

Technical Field

[0001] This invention relates to the field of building environment and noise control, and in particular to a noise assessment method applicable to collective living spaces such as university dormitories. Background Technology

[0002] Nighttime noise is a key environmental factor affecting the sleep quality and physical and mental health of college students. Current research and practice generally use A-weighted sound pressure level (Lo). Aeq While this physical quantity serves as a core evaluation indicator, it fails to characterize the spectral structure and temporal fluctuations of noise, severely underestimating the true interference level of non-steady-state, high-frequency noise, leading to a significant discrepancy between the evaluation results and subjective perceptions.

[0003] In terms of research methods, most existing studies rely on cross-sectional surveys and subjective questionnaires. While this method is easy to implement, the data is greatly affected by the personal factors of the participants, is highly subjective, and cannot provide objective acoustic parameters of noise, making it difficult to establish a precise causal relationship between noise and sleep disturbance, thus limiting the targeted nature of control measures.

[0004] Furthermore, existing evaluation methods typically treat dormitory noise as a single entity, failing to finely deconstruct the complex composition of sound sources, resulting in insufficiently targeted governance measures. In reality, sound sources such as human activities and equipment operation vary greatly in physical characteristics and perception. Vague evaluations and controls lead to inefficient noise reduction strategies and an inability to achieve "targeted governance."

[0005] More importantly, traditional evaluation systems completely ignore the heterogeneity of noise distribution in three-dimensional space. The regular rectangular layout of dormitories easily induces low-frequency standing waves, causing sound energy to accumulate in areas such as corners and bedside, resulting in uneven noise exposure levels for students in different beds. This spatial inequity is a blind spot in governance that existing methods that use "one-point measurement" to represent the overall acoustic environment cannot reveal. Current technologies lack a comprehensive consideration of both subjective noise perception and spatial distribution. Summary of the Invention

[0006] The technical problem this invention aims to solve is that existing methods for evaluating noise in university dormitories suffer from issues such as limited indicators, significant subjective bias, unclear sound source analysis, and neglect of spatial distribution. This invention provides a comprehensive evaluation method that integrates objective measurement, perceptual quantification, and spatial visualization. This method can accurately identify dominant noise sources, scientifically quantify their subjective annoyance levels, and intuitively reveal the spatial distribution patterns of noise within dormitories. Therefore, it provides a comprehensive scientific basis for developing precise and efficient noise control strategies, fundamentally overcoming the shortcomings of existing technologies.

[0007] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A multi-dimensional evaluation method for university dormitory noise based on psychoacoustics and sound field simulation, comprising a noise acquisition module, a psychoacoustic parameter analysis module, a sound field simulation module, and an optimization suggestion generation module connected in sequence. Each module works collaboratively to complete the multi-dimensional evaluation of dormitory noise and generate management suggestions. The specific technical solution is as follows:

[0008] The noise acquisition module is responsible for using a Class 1 precision sound level meter conforming to IEC 61672-1 and a high-fidelity recorder to simultaneously acquire equivalent continuous sound level, statistical sound level, maximum sound level, and complete raw audio signals at different locations during key nighttime periods in the dormitory (such as from 23:00 to 01:00 the next day), providing a high-precision physical data foundation for subsequent analysis.

[0009] The psychoacoustic analysis module integrates professional acoustic analysis software (such as Head Artemis) and, based on the Zwicker model of the ISO 532-1 standard, performs in-depth processing on the collected audio data, calculates and outputs core psychoacoustic parameters including time-varying loudness, sharpness, roughness, and fluctuation intensity, and finally, based on the Fastl-Zwicker model, fuses the above parameters to calculate a comprehensive index that fully reflects the degree of subjective annoyance to the human ear—psychoacoustic annoyance level.

[0010] The sound field simulation module utilizes multiphysics simulation software such as COMSOL Multiphysics to establish a 1:1 three-dimensional geometric model including furniture based on the measured dimensions and layout of the dormitory. By setting reasonable impedance boundary conditions and sound source attributes inverted from measured data, the module performs numerical simulation of the sound field at typical frequencies (such as 63Hz, 125Hz, 250Hz, and 500Hz). The numerical simulation obtains the sound pressure distribution that reflects the essence of the sound field, and then generates a sound pressure level distribution cloud map for visualization analysis, thereby clearly revealing the indoor standing wave phenomenon and the spatial concentration area of ​​sound energy.

[0011] The optimization suggestion generation module, based on the analysis results of physical and psychoacoustic parameter analysis and sound field simulation and spatial distribution analysis, conducts a multi-dimensional evaluation of the dormitory acoustic environment: from the perception dimension, based on the comprehensive annoyance level (P... A Various noise scenarios were ranked to identify the noise source type with the strongest subjective interference. From a spatial perspective, combined with sound field cloud maps, "hotspot areas" where sound energy is concentrated were located, especially the spatial relationship between the standing wave antinodes and the bed. Based on the above analysis, targeted noise reduction strategies were proposed: if the high level of annoyance originates from human activity noise, it is recommended to strengthen behavioral management; if spatial analysis shows that the head of the bed is located in a high sound pressure area, it is recommended to adjust the bed layout or add local sound-absorbing materials.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] 1. This invention constructs a multi-dimensional integrated evaluation system, realizing a full-chain diagnosis of noise. For the first time, this invention deeply integrates physical acoustic measurement, psychoacoustic analysis, and computational acoustic simulation, breaking through the limitations of existing single-dimensional evaluation technologies. It constructs a full-chain dormitory noise evaluation system covering "physical characteristics - subjective perception - spatial distribution." This system can not only obtain the objective physical parameters of noise but also quantify its subjective annoyance level, while visually presenting spatial distribution patterns. This enables a comprehensive and accurate diagnosis of complex noise environments, providing a multi-dimensional scientific basis for noise reduction strategy formulation.

[0014] 2. This invention improves the scientific rigor and reliability of the evaluation results. Psychoacoustic parameters (loudness, sharpness, roughness, and fluctuation intensity) are directly based on the human ear's auditory perception mechanism, enabling them to more accurately reflect the subjective interference differences between different types of noise, compared to the traditional A-weighted sound pressure level (L). Aeq The correlation between the overall annoyance level and subjective annoyance level is significantly higher. The overall annoyance level (P) calculated using the Fastl-Zwicker model is significantly higher. A This approach achieves objective quantification of subjective perception, effectively avoiding the influence of individual differences in subjective questionnaires, making the evaluation results more scientific, objective, and comparable. It can accurately distinguish the interference levels of different noise sources, providing a reliable basis for determining governance priorities.

[0015] 3. This invention achieves targeted noise control and improves noise reduction efficiency. The sound field simulation module calculates the sound pressure distribution and generates a visualized sound pressure level distribution cloud map, accurately revealing the spatial distribution patterns and hotspot areas of noise. It clarifies the impact of standing wave phenomena and sound source location on noise distribution, solving the problem that existing technologies cannot identify spatial distribution heterogeneity. Based on the targeted noise reduction strategy generated by multi-dimensional coupling analysis, it can accurately target the dominant noise source and key problem areas, achieving a leap from "general control" to "targeted control." This avoids the blindness of general noise reduction measures, significantly improves the targeting and efficiency of noise control, and reduces control costs.

[0016] 4. This invention combines practicality and operability, and has a wide range of applications. The technical solution of this invention is based on standardized measurement equipment and mature simulation software. The operation process is clear and standardized, and the data acquisition and analysis process is highly replicable. It requires no complex specialized equipment, making it convenient for practical application by university logistics management departments, building acoustics testing institutions, and other organizations. Furthermore, this method is applicable to university dormitories with different layouts (such as four-person rooms, six-person rooms, bunk beds with desks underneath, and bunk beds), and can also be extended to noise assessment in other collective living spaces such as nursing homes and staff dormitories, demonstrating its broad applicability and promotional value.

[0017] 5. This invention promotes multi-stakeholder collaboration in noise control. The "Dormitory Acoustic Environment Diagnosis and Optimization Report" generated by this invention not only provides management departments with a scientific basis for governance decisions, but also provides students with a clear understanding of noise impact and guidance for self-discipline. At the same time, it provides architectural design units with design references for optimizing the dormitory acoustic environment, promoting collaborative governance among management departments, students, and design units, and helping to improve the acoustic environment quality of collective living spaces from the source. Attached Figure Description

[0018] Figure 1 This is a block diagram of the overall system structure of the present invention;

[0019] Figure 2 This is a flowchart of the psychoacoustic parameter calculation process of the present invention;

[0020] Figure 3 This is a schematic diagram of the three-dimensional acoustic simulation model of the dormitory of the present invention;

[0021] Figure 4 This is a spatial distribution cloud map of sound pressure at typical frequencies of this invention. Detailed Implementation

[0022] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0023] This embodiment provides a multi-dimensional evaluation method for noise in university dormitories based on psychoacoustics and sound field simulation, which is implemented according to the following steps:

[0024] S1: On-site data acquisition and typical scenario construction

[0025] S1.1 In this embodiment, a representative four-person dormitory in a university student dormitory is selected as the implementation object. The dormitory has a bunk bed and desk layout, with uniform dimensions of 7.3m long × 3.3m wide × 3.15m high. The room is equipped with 4 bunk beds with desks (the headboard is placed against the wall), a wardrobe, and a private bathroom (with shower function). The air conditioner model is Midea KFR-35GW, which is installed on the wall of the dormitory next to the balcony.

[0026] S1.2 The acquisition equipment used is an AWA6228 Class 1 sound level meter (compliant with IEC 61672-1 standard) and a Kingson R77 high-fidelity voice recorder. The sound level meter is professionally calibrated, with a measurement range of 20-140dB(A), a frequency range of 20Hz-18kHz, and an accuracy of ±0.7dB. The voice recorder supports a 48kHz sampling rate, 24-bit lossless recording, a frequency response range of 20Hz-20kHz, and a signal-to-noise ratio ≥85dB.

[0027] S1.3. Collection Point Layout: Five collection points will be set up in each dormitory room: the geometric center of the dormitory (point C) and the headboards of the four beds (points B1-B4, 0.5m from the wall and 1.2m high). Sound level meters and recorders will be simultaneously deployed at each collection point, with the collection sequence being point C → point B1 → point B2 → point B3 → point B4. Each collection point will be used for continuous 2 hours of recording to ensure coverage of the core period from 23:00 to 01:00 the next day. The collection cycle will be 10 days, including 6 weekdays and 4 weekend days, to comprehensively cover the noise variation characteristics at different times.

[0028] S1.4 The data acquisition process strictly follows the operating procedures: the sound level meter is set to fast time weighting, with a 1-second sampling interval, continuously recording the A-weighted equivalent continuous sound level (Leq), cumulative percentage sound levels (L10, L50, L90, Lmax), and 1 / 1 octave band spectrum data from 31.5Hz to 8kHz; the voice recorder simultaneously records the raw audio signal and saves it in WAV format. During the acquisition process, a designated person regularly checks the equipment's operating status to avoid data loss or distortion caused by equipment relocation, power outages, or other reasons.

[0029] S1.5 Data Preprocessing and Typical Scene Construction: After data acquisition, acoustic analysis software was used to filter the raw data, removing invalid data such as equipment malfunctions and sudden abnormal noises (e.g., short-term construction noise outside the window), retaining only valid data samples. Through sound identification and sound level statistical analysis, eight typical mixed noise scene segments, each 30-60 seconds in length, were extracted from the valid audio data, covering three major noise types:

[0030] (1) Type 1: Washing + Air Conditioning Steady-State Noise Mixed Scene (3 segments), the main feature is the superposition of bathroom water flow sound, door closing sound and air conditioner operation noise, with rich mid-to-high frequency components;

[0031] (2) Type 2: Sudden snoring + steady-state air conditioning noise mixed scene (2 segments), the main feature is the superposition of intermittent snoring and steady-state air conditioning noise, with high fluctuation intensity;

[0032] (3) Type 3: Mixed scene of human activity sound + air conditioner steady-state noise (3 segments), the main features are the superposition of conversation sound, footstep sound, mobile phone external sound and air conditioner noise, with high sharpness and fluctuation intensity.

[0033] S2: Multi-parameter acoustic feature extraction and psychoacoustic analysis

[0034] S2.1 Physical Characteristics Analysis: Audio samples from eight typical noise scenarios were imported into Artemis Suite acoustic analysis software for 1 / 1 octave band spectrum analysis. The sound pressure level (SPL) of each frequency band (31.5 Hz to 8 kHz) was calculated, and the contribution rates of low-frequency (20-350 Hz), mid-frequency (350-2.8 kHz), and high-frequency (above 2.8 kHz) sound energy to the total sound energy were calculated using formulas. The results showed that in scenario type 1, the low-frequency contribution rate was 35%-42%, and the mid-to-high frequency contribution rate was 58%-65%; in scenario type 2, the low-frequency contribution rate was 45%-53%, and the mid-to-high frequency contribution rate was 47%-55%; and in scenario type 3, the low-frequency contribution rate was 28%-33%, and the mid-to-high frequency contribution rate was 67%-72%, clearly demonstrating the differences in the spectral distribution of different noise types.

[0035] S2.2 Psychoacoustic Parameter Calculation: Based on the ISO 532-1 standard, the Zwicker model was used to calculate the psychoacoustic parameters for each audio sample. First, the instantaneous loudness was calculated, and the total loudness was obtained by integrating over the 0-24 bar critical frequency band. The results showed that the total loudness of Type 3 scene was the highest (15-18 sone), followed by Type 2 scene (12-14 sone), and the lowest was Type 1 scene (10-12 sone). Further analysis of the loudness distribution in each bar band revealed that the loudness proportion of Type 3 scene in the 15-20 bar (mid-high frequency) range was significantly higher than other types, accounting for 40%-45% of the total loudness.

[0036] S2.3 Calculation of sharpness, roughness and fluctuation intensity: The sharpness of the Type 3 scenario is the highest (1.2-1.5 acum), mainly due to the high proportion of mid-to-high frequency components; the fluctuation intensity of the Type 2 scenario is the highest (0.8-1.0 vacil), which is consistent with the intermittent fluctuation characteristics of snoring; the roughness of the Type 1 scenario is the lowest (0.1-0.2 asper), due to the high proportion of steady-state noise from the air conditioner and the low modulation frequency.

[0037] S2.4, Calculation of Overall Annoyance Level: Based on the Fastl-Zwicker model, the psychoacoustic annoyance level (P) is calculated by substituting various psychoacoustic parameters. A The results show that scenario P is of type 3. A The highest value was found in scenario type 1 (8.5-9.2au), followed by scenario type 1 (6.8-7.5au), and the lowest was found in scenario type 2 (5.6-6.3au), clearly indicating that scenarios with mixed human activity sounds were the dominant noise source with the strongest subjective interference. A comparison with the results of a subjective questionnaire survey conducted during the same period showed that P... A The correlation coefficient between the value and the students' subjective annoyance score reached 0.87, which was significantly higher than the correlation coefficient between the A-weighted sound pressure level and the subjective score (0.62), verifying the reliability of the psychoacoustic parameter evaluation.

[0038] S3: Sound Field Modeling and Spatial Distribution Simulation

[0039] S3.1 3D Geometric Modeling: Based on the actual dimensions (7.3m long × 3.3m wide × 3.15m high) and internal layout of the target dormitory, a 1:1 3D geometric model was created in COMSOL Multiphysics software. The model accurately reproduced the positions of the four beds, desks, wardrobes, toilet, and doors and windows, ensuring that the spatial relationships of the furniture and facilities were consistent with reality.

[0040] S3.2 Mesh Generation: Tetrahedral meshes were used for adaptive mesh generation. Local meshing was performed in areas with drastic sound field changes, such as the sound source location, corners, and headboard areas. The minimum mesh unit size was 0.05m and the maximum was 0.2m. The final total number of mesh units was 863,000, and the mesh skewness was greater than 0.68, which met the calculation accuracy requirements.

[0041] S3.3, Physics Field Settings:

[0042] (1) Sound source settings: The washing sound source of the Type 1 scenario is simplified to a point sound source located in the center of the bathroom, and its sound power level is inverted according to the spectrum analysis results of the S2 stage (the sound power level range of each frequency band is 65-85dB); the air conditioner operation sound source is simplified to a surface sound source located at the air conditioner outlet, and the sound power level is set to 55-70dB according to the measured data.

[0043] (2) Boundary condition setting: Set reasonable impedance boundary conditions according to the actual building materials and furniture of the dormitory, such as walls (plastered brick walls, sound absorption coefficient 0.08@250Hz), floors (tiles, 0.02@250Hz), doors and windows (hard sound field boundary, 0.01) and furniture surfaces (wood, 0.15@250Hz).

[0044] S3.4 Simulation and Model Verification: Four typical frequencies—63Hz, 125Hz, 250Hz, and 500Hz—were selected for sound field simulation. After the simulation, the sound pressure level data at the geometric center (point C) of the dormitory was extracted and compared with the measured values ​​from stage S1. The results showed that the absolute error between the simulated and measured values ​​at each frequency was between 1.2 and 1.8 dB, less than the accuracy requirement of 3 dB, proving the reliability of the model.

[0045] S3.5 Spatial Distribution Analysis: The sound pressure distribution obtained from sound field simulation was analyzed, and sound pressure level distribution cloud maps at each frequency were output. The results show that there are obvious standing wave phenomena at 63Hz and 125Hz frequencies. The antinodes of the standing waves are mainly concentrated in the two diagonal corners of the dormitory and the area near the toilet, where the sound pressure level is 3-5dB higher than other areas. The standing wave phenomenon weakens at 250Hz and 500Hz frequencies, but the vicinity of the sound source (toilet door, under the air conditioner) is still a sound pressure level hotspot. Among the four beds, bed B3 (near the toilet and air conditioner) has the highest sound pressure level at all frequencies, while bed B1 (far from the sound source and corners) has the lowest sound pressure level. The noise exposure differences between different beds are significant, verifying the existence of spatial distribution heterogeneity.

[0046] S4: Comprehensive Diagnosis and Optimization Strategy Generation and Output

[0047] S4.1 Comprehensive Diagnostic Analysis: Combining the analysis results of S2 and S3, a multi-dimensional coupled diagnosis is conducted. In terms of perception, Type 3 (mixed human activity sound scene) is identified as the core dominant noise source. Its high annoyance is mainly due to the high sharpness caused by the high proportion of mid-to-high frequency components, and the large fluctuation intensity caused by intermittent fluctuations. In terms of spatial dimension, bed B3 is the most severely exposed to noise because it is located in both the standing wave antinode region and near the sound source, while bed B1 has the lowest noise exposure, indicating obvious spatial unfairness.

[0048] S4.2 Optimization Strategy Generation:

[0049] (1) Behavioral management recommendations: Formulate the "Dormitory Nighttime Quiet Agreement" to clearly define the quiet period after 23:00 and prohibit loud conversations, playing audio aloud, and vigorous washing activities; establish a noise monitoring and reminder mechanism led by the dormitory head; carry out noise science popularization to enhance students' awareness.

[0050] (2) Recommendations for architectural acoustic optimization: Install low-frequency sound absorbers (0.5m×0.5m×0.2m in size, sound absorption coefficient ≥0.85@63-125Hz) in the diagonal corners of the dormitory walls (standing wave antinodes); attach porous sound-absorbing panels (50mm thick, density 32kg / m³) to the headboard wall of bed B3. 3 (Sound absorption coefficient ≥0.7@250-500Hz); Replace door and window sealing strips to improve sealing performance.

[0051] (3) Suggestions for spatial layout adjustment: For dormitories with adjustable layout, swap the B3 bed with the B1 bed to keep the head of the bed away from the standing wave antinode and sound source; adjust the position of the desk and wardrobe and adopt an asymmetrical layout to break the regular sound field mode; when designing new dormitories, optimize the installation position of the toilet and air conditioner to avoid them being close to the bed area.

[0052] S4.3 Report Generation: Integrate the above analysis process, data, charts, conclusions, and optimization strategies to generate the "Dormitory Acoustic Environment Diagnosis and Optimization Report." The report has a clear structure and uses a combination of charts to ensure that management and students can quickly understand and apply it.

[0053] S4.4 Implementation Effect Prediction: By implementing the above optimization strategies, it is expected that the psychoacoustic annoyance level (P) of the dominant noise source (human activity sound) will be reduced. A The sound pressure level in the dormitory will be reduced by 30%-40%, and the sound pressure level in the high sound pressure level area caused by low-frequency standing waves will be reduced by 2-3 dB. The noise exposure difference between different beds will be reduced to 1-2 dB, and the overall sound environment quality of the dormitory and students' subjective satisfaction will be significantly improved.

[0054] The above-described embodiments are merely one implementation of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the claims.

Claims

1. A multi-dimensional evaluation method for noise in university dormitories based on psychoacoustics and sound field simulation, characterized in that, Includes the following steps: S1: Noise data acquisition steps: At multiple preset points inside the dormitory, use a Class 1 precision sound level meter conforming to IEC 61672-1 standard and a high-fidelity recorder to simultaneously acquire equivalent continuous A-weighted sound level, statistical sound level, maximum sound level and original audio signal. S2: Psychoacoustic analysis step. Based on the Zwicker model of ISO 532-1 standard, the original audio signal is processed to calculate psychoacoustic parameters such as loudness, sharpness, roughness, and fluctuation intensity. These parameters are then fused using the Fastl-Zwicker model to calculate the psychoacoustic annoyance level (P), which characterizes the degree of subjective annoyance to the human ear. A ); S3: Sound field simulation step. Based on the actual size and internal layout of the dormitory, a three-dimensional geometric model is established. Based on the measured data in step S1, the sound source attributes are inverted and boundary conditions are set. The sound field at typical frequencies is numerically simulated. The standing wave phenomenon is revealed by calculating the sound pressure distribution, and a sound pressure level distribution cloud map is generated to characterize the spatial distribution of sound energy. S4: Optimize the strategy generation step, taking into account the psychoacoustic annoyance level (P). A Based on the analysis results of the sound pressure level distribution cloud map, the dominant noise sources and hotspots where sound energy is concentrated are identified, and targeted noise reduction strategies including at least one of behavior management, architectural acoustics optimization and spatial layout adjustment are generated.

2. The method according to claim 1, characterized in that, In step S1, the preset points include the geometric center of the dormitory and the head of each bed; the data collection is carried out between 23:00 at night and 01:00 the next day.

3. The method according to claim 1, characterized in that, Before step S2, there is also a typical noise scene construction step: by listening to and identifying the effective audio data and performing sound level statistical analysis, typical noise scene segments including human activity sounds, equipment operation sounds and their mixtures are extracted and classified.

4. The method according to claim 1, characterized in that, In step S3, the three-dimensional geometric model is a 1:1 scale model and includes the interior structure of the bed, desk, wardrobe, doors and windows, and bathroom; the numerical simulation adopts the finite element method, and the typical frequencies include at least 63Hz, 125Hz, 250Hz, and 500Hz.

5. The method according to claim 1 or 4, characterized in that, In step S3, the mesh is locally refined at the sound source location, corners, and headboard areas; and after simulation, the sound pressure level data of the geometric center point of the dormitory obtained from the simulation is compared with the measured value to ensure that the absolute error at each frequency is less than 3dB.

6. The method according to claim 1, characterized in that, In step S4, identifying the dominant noise source refers to: based on the psychoacoustic annoyance level (P) A The different types of noise scenarios are sorted, and the noise type with the highest PA value is identified as the dominant noise source.

7. The method according to claim 1, characterized in that, In step S4, identifying the hotspot area where sound energy is concentrated means: using the sound pressure level distribution cloud map, locating the area where the sound pressure level is significantly higher than the average indoor sound pressure level by more than 3dB, and making a comprehensive judgment in combination with the standing wave antinode area.

8. The method according to claim 1, characterized in that, In step S4, the architectural acoustic optimization includes installing low-frequency sound absorbers in the standing wave antinodes region determined by sound field simulation, or laying porous sound-absorbing panels in the headboard area where noise exposure levels are high.

9. The method according to claim 1, characterized in that, In step S4, the spatial layout adjustment includes swapping beds located in the sound energy hotspot area with beds located in the low sound pressure area, or using an asymmetrical layout of furniture to break the regular sound field mode.

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