A square dance noise data processing method

Abstract

The present invention relates to a square dance noise data processing method, which relates to the field of smart cities, including: obtaining a set of probabilities of residents being at home in a noise coverage area according to the current playback interval matching; utilizing a noise area simulation space, based on current meteorological data and a set of probabilities of residents being at home, taking the range of the movable area of the audio as the optimization space, and iteratively optimizing the audio layout parameters with the goal of minimizing the overall noise impact, and outputting the optimal position and optimal angle; placing the audio within the current playback interval according to the optimal position and optimal angle, and continuing to optimize the audio placement. The present invention can solve the technical problem that traditional methods cannot reasonably select the activity venue and audio layout of square dance in combination with actual scenarios, resulting in the difficulty in effectively controlling the regional noise impact; it can improve the scientificity and rationality of the setting of the square dance activity venue and audio layout, minimize regional noise pollution, and realize intelligent and scientific urban noise management.

Description

Technical Field

[0001] The present invention relates to the field of smart cities, and in particular to a method for processing square dance noise data. Background Art

[0002] Square dancing, as a widely popular mass fitness activity, has strong social attributes and fitness value. However, due to the high-decibel noise generated by its audio equipment, it can easily cause interference to surrounding residents, especially in open spaces such as residential areas, parks, and squares.

[0003] The existing square dance noise management methods mainly rely on passive management measures such as fixed volume limits, manual inspections, and resident complaints and feedback, which makes it difficult to achieve scientific, accurate, and efficient noise optimization management. Summary of the Invention

[0004] The present invention aims to solve the technical problem that traditional methods cannot reasonably select square dance activity venues and sound layouts based on actual scenarios, resulting in difficulty in effectively controlling regional noise impacts. A square dance noise data processing method is provided to solve the problem.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: The present invention provides a square dance noise data processing method, comprising: dividing the scheduled activity time period of square dance into multiple playback intervals, and obtaining a probability set of residents at home in the noise coverage area according to the current playback interval matching; constructing a noise area simulation space according to the architectural characteristics of the noise coverage area and the location coordinates of several households; utilizing the noise area simulation space, based on current meteorological data and the probability set of residents at home, taking the range of the movable area of the audio as the optimization space, and iteratively optimizing the audio layout parameters with the goal of minimizing the overall noise impact, and outputting the current optimal layout parameters, wherein the current optimal layout parameters include the optimal position and the optimal angle; placing the audio in the current playback interval according to the optimal position and the optimal angle, and continuing to optimize the audio placement in the order of the playback intervals until the end of the square dance activity.

[0006] Optionally, the square dance noise data processing method also includes: performing noise diffusion coverage simulation based on the maximum noise intensity of the square dance within a preset time period and the characteristics of the surrounding buildings to determine the noise coverage area, wherein the default regional wind speed is 0 during the noise diffusion simulation; dividing the scheduled activity time period of the square dance according to a predetermined time step to determine multiple playback intervals; separately counting the historical proportion of residents at home for several households in the noise coverage area under the multiple playback intervals, setting them as the historical resident at home probability, obtaining multiple historical resident at home probability sets, and establishing a probability mapping relationship between the playback interval and the historical resident at home probability set; obtaining the resident at home probability set based on matching the current playback interval and the probability mapping relationship.

[0007] Optionally, the square dance noise data processing method also includes: obtaining the range of the movable audio area for square dancing in the area, and randomly selecting multiple audio placement positions within the movable audio area; configuring multiple audio placement angles according to predetermined angle intervals, and randomly combining the multiple audio placement angles and several audio placement positions to generate several initial layout parameters; utilizing the noise area simulation space, according to current meteorological data and the probability set of residents being at home, with the goal of minimizing the overall noise impact, iteratively optimizing the audio layout parameters according to the several initial layout parameters, and outputting the current optimal layout parameters.

[0008] Optionally, the square dance noise data processing method also includes: utilizing the noise area simulation space and combining it with a BP neural network to construct a noise diffusion simulator; monitoring and acquiring current meteorological data, wherein the meteorological data includes wind speed and wind direction; inputting the current meteorological data and a number of initial layout parameters into the noise diffusion simulator, and outputting a number of noise simulation intensity sets, wherein each noise simulation intensity set includes a number of noise simulation intensities of a number of households; calculating a number of overall influence coefficients based on the number of noise simulation intensity sets and the probability sets of residents being at home; iteratively optimizing the sound layout parameters based on the number of initial layout parameters and the number of overall influence coefficients, and outputting the current optimal layout parameters.

[0009] Optionally, the square dance noise data processing method also includes: constructing an initial noise diffusion simulator based on the noise area simulation space and the BP neural network as the prediction logic; collecting sample meteorological data sets and sample audio layout parameter sets according to regional noise monitoring records, and counting several noise intensities of several households under different sample meteorological data and sample audio layout parameters to obtain multiple sample noise intensity sets; using the sample meteorological data sets and sample audio layout parameter sets as input and the multiple sample noise intensity sets as supervision, training the initial noise diffusion simulator until convergence to obtain the noise diffusion simulator.

[0010] Optionally, the square dance noise data processing method also includes: establishing a location mapping association between the probability of residents being at home and the noise simulation intensity; based on the location mapping association, taking the probability of residents being at home as the weight, performing weighted calculations on the several noise simulation intensity sets according to the resident home probability sets, and outputting several overall impact coefficients.

[0011] Optionally, the square dance noise data processing method further includes: mapping and arranging the initial layout parameters according to the coefficients from small to large based on the several overall influence coefficients to generate an initial parameter sequence; setting the initial parameters as the initial solution, and setting the first N initial solutions of the initial parameter sequence as the optimal solution, and setting the last M initial solutions as the inferior solution, wherein M is K times N, and K is an integer greater than 10 and less than 50; randomly clustering the M inferior solutions with the N optimal solutions as the center to generate N solution sets, and calculating N adjustment step sets; within the N solution sets, taking the optimal solution as the adjustment direction, according to the N adjustment step sets The inferior solutions in the solution set are optimized and adjusted, and N updated solution sets are output. If the adjusted inferior solution exceeds the range of the movable area of the audio system, no adjustment is performed. The overall influence coefficients of the inferior solutions in the N updated solution sets are calculated, and the N updated solution sets are identified. If the overall influence coefficient of the inferior solution is smaller than the overall influence coefficient of the superior solution in the same updated solution set, the superior solution is replaced by the inferior solution. The iterative optimization is continued until a predetermined number of optimization times is reached, and N current updated solution sets are output. The current updated solution set with the smallest sum of the overall influence coefficients is selected as the optimal solution set, and the superior solution of the optimal solution set is set as the current optimal layout parameter.

[0012] Optionally, the square dance noise data processing method also includes: configuring an initial adjustment step, wherein the initial adjustment step includes a position adjustment amplitude and an angle adjustment amplitude; randomly selecting a first solution set, and obtaining the first optimal solution overall influence coefficient of the first optimal solution in the first solution set, as well as multiple first inferior solution overall influence coefficients of multiple inferior solutions; respectively calculating the deviation ratio of the multiple first inferior solution overall influence coefficients to the first optimal solution overall influence coefficient, and setting multiple first correction weights according to the deviation ratio, wherein the deviation ratio and the correction weight are positively correlated; optimizing and correcting the initial adjustment step according to the multiple first correction weights to obtain a first adjustment step set, and adding it to the N adjustment step sets.

[0013] The beneficial effects of the present invention are as follows: by dividing the scheduled activity time period of square dancing into multiple playback intervals, the probability set of residents being at home in the noise coverage area is obtained according to the current playback interval matching; then a noise area simulation space is constructed according to the architectural characteristics of the noise coverage area and the location coordinates of several families; further utilizing the noise area simulation space, according to current meteorological data and the probability set of residents being at home, the audio movable area range is used as the optimization space, and the audio layout parameters are iteratively optimized with the goal of minimizing the overall noise impact, and the current optimal layout parameters are output, wherein the current optimal layout parameters include the optimal position and the optimal angle; finally, the audio is placed in the current playback interval according to the optimal position and the optimal angle, and the audio placement optimization is continued in the order of the playback intervals until the square dance activity ends; the above method can improve the scientificity and rationality of the square dance activity venue and audio layout setting, so that while ensuring the entertainment experience, the regional square dance noise pollution can be minimized, and intelligent and scientific urban noise management can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic flow chart of a square dance noise data processing method provided by the present invention;

[0015] Figure 2 A schematic diagram of the flow of obtaining a probability set of residents being at home in a square dance noise data processing method provided by the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0018] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0019] Examples, such as Figure 1 As shown, an embodiment of the present invention provides a method for processing square dance noise data, which specifically includes the following steps:

[0020] S100: Divide the scheduled activity time period of square dancing into multiple playing intervals, and obtain a probability set of residents in the noise coverage area being at home based on the current playing interval matching.

[0021] Further, if Figure 2 As shown, step S100 of the present invention further includes:

[0022] S110: Perform noise diffusion coverage simulation based on the maximum noise intensity of square dancing within a preset time period and the characteristics of surrounding buildings to determine the noise coverage area, wherein the default regional wind speed during the noise diffusion simulation is 0; S120: Divide the scheduled activity time period of square dancing according to the predetermined time step to determine multiple playback intervals; S130: Count the historical proportions of residents at home of several households in the noise coverage area under the multiple playback intervals, set them as the historical resident at home probability, obtain multiple historical resident at home probability sets, and establish a probability mapping relationship between the playback interval and the historical resident at home probability set; S140: Obtain the resident at home probability set based on the current playback interval and the probability mapping relationship.

[0023] Specifically, the maximum noise intensity of square dance is first obtained based on noise monitoring records from square dances within a preset time period (e.g., the last month), such as noise data recorded during square dance activities using noise monitoring equipment. Next, the building characteristics surrounding the square dance activity area are obtained, including building geometry (building height, building density, etc.) and vegetation information (tree height, etc.). Noise diffusion simulation conditions are then set, with the default regional wind speed set to 0 during the noise diffusion simulation process, ignoring the influence of wind speed and direction. Based on these conditions, noise diffusion coverage is simulated based on the maximum noise intensity and surrounding building characteristics. For example, ray tracing is used to simulate the noise propagation path in the building environment, and a noise attenuation model is used to calculate the noise coverage range to obtain the noise coverage area.

[0024] On the other hand, the scheduled activity time period of square dancing is obtained, such as the square dancing activity period is usually from 06:00 to 08:00 and from 18:00 to 22:00; then the scheduled time step is set, usually taking a value of 10 to 30 minutes to ensure that the optimization calculation is fine enough and reduce the computational burden, such as setting the scheduled time step to 15 minutes; then the scheduled activity time period of square dancing is divided according to the scheduled time step, as shown in Table 1, to obtain multiple playback intervals.

[0025]

[0026] Table 1

[0027] Next, based on the square dance noise monitoring records, the historical home percentages of several households within the noise coverage area during the multiple playback intervals are counted, such as through a questionnaire survey. Based on the statistical data, the home percentage for each playback interval is calculated. The home percentage is the ratio of the number of home visits to the total number of visits. For example, the number of home visits during the same playback interval each day is counted to obtain the historical home percentage, which is set as the historical home probability. Multiple historical home probability sets are obtained, and a probability mapping relationship is established between the playback intervals and the historical home probability sets. The current playback interval (e.g., 06:00 to 06:15) is then read, and the home probability set corresponding to the current playback interval is matched according to the probability mapping relationship.

[0028] S200: Constructing a noise area simulation space according to the architectural features of the noise coverage area and the location coordinates of a plurality of households.

[0029] Specifically, the architectural feature information of the noise coverage area is obtained, including data such as building geometric features, building density and vegetation information, as well as the location coordinates of several households in the noise coverage area; then three-dimensional simulation modeling is performed based on the architectural feature information and the location coordinates of several households, such as defining a three-dimensional coordinate system, setting the square dance activity location as the origin, importing building data, generating a three-dimensional noise propagation environment, and loading household location data, mapping it into three-dimensional space, and generating a noise area simulation space.

[0030] S300: Utilizing the noise area simulation space, based on current meteorological data and the probability set of residents being at home, with the range of the movable area of the audio system as the optimization space, and with the goal of minimizing the overall noise impact, iteratively optimizing the audio layout parameters, outputting the current optimal layout parameters, wherein the current optimal layout parameters include the optimal position and the optimal angle.

[0031] Furthermore, step S300 of the present invention further includes:

[0032] S310: Obtain the range of the movable audio area for square dancing in the area, and randomly select multiple audio placement positions within the movable audio area; S320: Configure multiple audio placement angles according to predetermined angle intervals, randomly combine the multiple audio placement angles and multiple audio placement positions, and generate multiple initial layout parameters.

[0033] Specifically, first, the range of the movable audio area for square dancing in the area is obtained. The movable audio area refers to the area within the square dance activity area where the audio can be effectively placed; then, a plurality of audio placement positions are randomly selected within the movable audio area, such as dividing the movable audio area into several small units, and randomly selecting a plurality of placement positions from the divided small units. A reasonable number of random selections is determined according to the size of the venue to ensure that the selected audio positions within the movable audio area are sufficiently diverse so as to optimize the selection of the best layout.

[0034] Then configure the predetermined angle interval, which can be set according to the actual scenario, such as configuring a placement angle every 15° to ensure that the sound can cover different areas; and configure multiple speaker placement angles according to the predetermined angle interval, that is, set a speaker placement angle every 15°; then randomly combine the multiple speaker placement angles and several speaker placement positions to generate several initial layout parameters. Considering the limitations of site conditions, some unreasonable placement angles can be eliminated according to the speaker placement position. For example, if there are buildings or other obstacles near the speaker placement position, it will affect the propagation direction of the sound waves. Therefore, placement positions at certain angles should be eliminated to ensure that the generated initial layout parameters are reasonable and practical.

[0035] S330: Utilizing the noise area simulation space, based on current meteorological data and the probability set of residents being at home, with the goal of minimizing the overall noise impact, iteratively optimizing the sound layout parameters based on the several initial layout parameters, and outputting the current optimal layout parameters.

[0036] Furthermore, step S330 of the present invention further includes:

[0037] S331: Utilize the noise region simulation space and combine it with the BP neural network to construct a noise diffusion simulator.

[0038] Furthermore, step S331 of the present invention further includes:

[0039] S3311: Based on the noise area simulation space, an initial noise diffusion simulator is constructed using a BP neural network as the prediction logic; S3312: Based on the regional noise monitoring records, a sample meteorological data set and a sample acoustic layout parameter set are collected, and statistics are collected on a number of noise intensities of a number of households under different sample meteorological data and sample acoustic layout parameters to obtain multiple sample noise intensity sets; S3313: Using the sample meteorological data set and sample acoustic layout parameter set as input and the multiple sample noise intensity sets as supervision, the initial noise diffusion simulator is trained until convergence to obtain the noise diffusion simulator.

[0040] Specifically, based on the noise area simulation space, an initial noise diffusion simulator is constructed using the BP neural network as the prediction logic, that is, the BP neural network (back propagation neural network) is used as a prediction model, combined with the noise area simulation space, to simulate the diffusion of noise through training. The initial noise diffusion simulator includes an input layer, multiple hidden layers (used to learn the nonlinear relationship between input and output) and an output layer. The input data of the input layer are meteorological data (wind speed and direction) and audio layout parameters (placement position and placement angle), and the output data of the output layer are the noise intensity of several households in the noise coverage area.

[0041] Next, based on the regional noise monitoring records, sample meteorological data sets and sample sound layout parameter sets are collected, where each change in the sound position or angle will affect the noise diffusion range, and the wind speed and wind direction will also have an impact on the noise diffusion; then, the noise intensities of several households under different sample meteorological data and sample sound layout parameters are statistically analyzed to obtain multiple sample noise intensity sets. Then, the sample meteorological data and sample sound layout parameters are used as input, and the sample noise intensity set is used as supervision. The sample meteorological data set, sample sound layout parameter set and sample noise intensity set are used as training data to perform supervised training on the initial noise diffusion simulator. First, the sample meteorological data and sample sound layout parameters are passed as input to the neural network. The network generates a predicted value through calculations at each layer, predicts the noise intensity, and calculates the difference between the predicted noise intensity and the actual noise intensity as the model error; then, a loss function (such as mean square error MSE) is used to calculate the error between the noise intensity output by the model and the actual noise intensity, and the error is calculated by inverse The forward propagation algorithm calculates the gradient of the error with respect to each layer of network parameters (weights and biases). Gradient descent (or other optimization algorithms) is then used to update the network parameters to minimize the model output error. Based on the results of backpropagation, an optimization algorithm (such as gradient descent or Adam) is then used to update the network weights and biases, adjusting the network parameters to bring the predicted values closer to the actual noise intensity. This process (forward propagation, error calculation, backpropagation, and weight update) is repeated for multiple rounds of training. Training stops when the training error reaches a set minimum or the number of training iterations reaches a predetermined upper limit, resulting in a trained noise diffusion simulator. The trained noise diffusion simulator can intelligently predict noise intensity and diffusion range based on given meteorological conditions and acoustic layout parameters, providing a scientific basis for acoustic layout optimization.

[0042] S332: Monitor and obtain current meteorological data, wherein the meteorological data includes wind speed and wind direction; S333: Input the current meteorological data and several initial layout parameters into the noise diffusion simulator, and output several noise simulation intensity sets, wherein each noise simulation intensity set includes several noise simulation intensities of several households.

[0043] Specifically, current meteorological data, including wind speed (wind speed in the current area, typically in meters per second) and wind direction (wind direction, typically expressed as an angle, with due north at 0° and increasing clockwise), is acquired through a monitoring tool. This current meteorological data is then combined with several initial layout parameters to generate multiple input data (including the current meteorological data and any initial layout parameters). This input data is then input into the noise diffusion simulator, which then outputs several sets of simulated noise intensities, each of which includes several simulated noise intensities for several households.

[0044] S334: Calculate a plurality of overall impact coefficients based on the plurality of noise simulation intensity sets and the plurality of resident at-home probability sets.

[0045] Furthermore, step S334 of the present invention further includes:

[0046] S3341: Establish a location mapping association between the probability of residents being at home and the noise simulation intensity; S3342: Based on the location mapping association, take the probability of residents being at home as the weight, perform weighted calculations on the several noise simulation intensity sets according to the resident home probability set, and output several overall impact coefficients.

[0047] Specifically, a location mapping association is established for each household's noise simulation intensity and the probability of the household being at home. The noise simulation intensity of each household can be associated with the noise simulation intensity through the household's location coordinates (such as longitude and latitude) to obtain a noise impact value for each household. The noise simulation intensity value for each household will be adjusted according to the probability of the household being at home, with the purpose of improving the assessment of the impact of residents' actual exposure to noise. Then, based on the location mapping association, the probability of the resident being at home is used as the weight, and the several noise simulation intensity sets are weighted according to the resident at home probability set. That is, for each playback interval (such as a certain time period of each day), the weighted noise intensity of all households in the area is summed. The weighted noise intensity of each household is obtained by multiplying the noise simulation intensity and the resident at home probability. The overall impact coefficient for the time period is calculated. This overall impact coefficient reflects the comprehensive noise impact of square dancing activities on all households within a specific playback interval.

[0048] By weighting the probability of residents being at home and the noise simulation intensity, the noise impact value of each household in a specific playback range can be obtained, and the overall impact coefficient can be calculated. This process can help evaluate the actual impact of different playback ranges and audio layout schemes on square dance noise, and provide a scientific basis for the optimization of audio placement.

[0049] S335: performing iterative optimization of the acoustic layout parameters according to the plurality of initial layout parameters and the plurality of overall influence coefficients, and outputting the current optimal layout parameters.

[0050] Furthermore, step S335 of the present invention further includes:

[0051] S3351: Based on the several overall influence coefficients, the several initial layout parameters are mapped and arranged according to the coefficients from small to large to generate an initial parameter sequence; S3352: The initial parameters are set as the initial solutions, and the first N initial solutions of the initial parameter sequence are set as optimal solutions, and the last M initial solutions are set as inferior solutions, where M is K times N, and K is an integer greater than 10 and less than 50; S3353: The M inferior solutions are randomly clustered with the N optimal solutions as the center to generate N solution sets, and N adjustment step sets are calculated.

[0052] Specifically, based on the several overall influence coefficients, the several initial layout parameters are mapped and arranged from small to large according to the coefficients to generate an initial parameter sequence. The smaller the influence coefficient, the lower the noise impact and the better the layout; the larger the influence coefficient, the greater the noise impact of the layout scheme and the need for optimization and adjustment. Then, the initial parameters are set as the initial solution, and the first N initial solutions of the initial parameter sequence are set as the optimal solution, and the last M initial solutions are set as the inferior solution, where M and N are both positive integers, M is K times N, and K is an integer greater than 10 and less than 50, which can be set according to actual conditions, such as setting K to 20; then, the M inferior solutions are randomly clustered with the N optimal solutions as the center to generate N solution sets, with the same number of inferior solutions in each solution set, and N adjustment step sets of the N solution sets are calculated.

[0053] Furthermore, step S3353 of the present invention further includes:

[0054] S33531: Configure the initial adjustment step, wherein the initial adjustment step includes the position adjustment amplitude and the angle adjustment amplitude; S33532: Randomly select the first solution set, and obtain the first optimal solution overall influence coefficient of the first optimal solution in the first solution set, as well as multiple first inferior solution overall influence coefficients of multiple inferior solutions; S33533: Calculate the deviation ratio of the multiple first inferior solution overall influence coefficients to the first optimal solution overall influence coefficient respectively, and set multiple first correction weights according to the deviation ratio, wherein the deviation ratio and the correction weight are positively correlated; S33534: Optimize and correct the initial adjustment step according to the multiple first correction weights to obtain a first adjustment step set, and add it to the N adjustment step sets.

[0055] Specifically, an initial adjustment step is configured, including a position adjustment amplitude and an angle adjustment amplitude. The position adjustment amplitude is used to change the placement of the speaker, ensuring that the speaker gradually approaches the optimal solution during the optimization process. This adjustment can be set based on actual conditions, such as setting the position adjustment amplitude to 0.2 meters. The angle adjustment amplitude is used to optimize the speaker's pointing angle and reduce unnecessary noise diffusion, such as setting the angle adjustment amplitude to 5 degrees. Then, any solution set is randomly selected from the N solution sets as the first solution set, and the overall influence coefficient of the first optimal solution in the first solution set is obtained, as well as the overall influence coefficients of the multiple first inferior solutions of the multiple inferior solutions.

[0056] The deviation ratios of the overall impact coefficients of the multiple first inferior solutions and the overall impact coefficient of the first optimal solution are then calculated. The deviation ratio is the ratio of the difference between the overall impact coefficients of the first inferior solution and the first optimal solution to the overall impact coefficient of the first optimal solution, indicating the increase in the noise impact of a particular inferior solution compared to the optimal solution. Multiple first correction weights are then set based on the deviation ratios. The deviation ratio and the correction weight are positively correlated, meaning that the smaller the deviation ratio, the lower the weight, the smaller the adjustment step size, and the improved optimization accuracy; the larger the deviation ratio, the higher the weight, the larger the adjustment step size, and the improved optimization efficiency. The correction weight determines the step size of each inferior solution in the optimization adjustment, allowing the optimization process to balance accuracy and efficiency. By calculating the deviation ratio of the overall impact coefficient and setting the correction weights, the optimization process can be made more intelligent, achieving both efficient and accurate parameter optimization.

[0057] The initial adjustment step size is further optimized and corrected based on the multiple first correction weights. Specifically, the initial adjustment step size is multiplied by the first correction weight, and the product of the two is used as the first adjustment step size to obtain a first adjustment step size set. The same method is then used to calculate the N adjustment step size sets for the N solution sets. By optimizing the adjustment step size with correction weights, intelligent and dynamic sound layout optimization is achieved, enabling rapid convergence of the optimization process while ensuring high precision in the final layout, thereby improving the scientific nature and rationality of square dance noise management.

[0058] S3354: within the N solution sets, taking the best solution as the adjustment direction, optimizing and adjusting the inferior solutions within the solution set according to the N adjustment step sets, and outputting N updated solution sets, wherein if the adjusted inferior solution exceeds the range of the movable area of the audio, no adjustment is performed; S3355: calculating the overall influence coefficient of the inferior solutions within the N updated solution sets, and identifying the N updated solution sets, if within the same updated solution set, the overall influence coefficient of the inferior solution is smaller than the overall influence coefficient of the superior solution, replacing the superior solution with the inferior solution; S3356: continuing to iterate and optimize until a predetermined number of optimizations is reached, outputting N current updated solution sets, and selecting the current updated solution set with the smallest sum of the overall influence coefficients as the optimal solution set, and setting the superior solution of the optimal solution set as the current optimal layout parameter.

[0059] Specifically, within the N solution sets, the optimal solution is used as the adjustment direction, and the inferior solutions within the solution set are optimized and adjusted according to the N adjustment step sets. If the adjusted inferior solution exceeds the range of the movable audio area, the adjustment is not performed to avoid an unreasonable audio layout due to excessive adjustment, thereby obtaining N updated solution sets. The overall influence coefficient of the inferior solutions within the N updated solution sets is then calculated, and the N updated solution sets are identified. If, within the same updated solution set, the overall influence coefficient of the inferior solution is less than that of the superior solution, the superior solution is replaced with the inferior solution, thus ensuring that the optimized superior solution always has the layout with the least noise impact. The same method is used to continue iterative optimization, repeating the optimization until a predetermined number of optimization attempts is reached. N current updated solution sets are output, and the current updated solution set with the smallest sum of the overall influence coefficients is selected as the optimal solution set. The superior solution of the optimal solution set is set as the current optimal layout parameter.

[0060] The optimization algorithm uses a correction weight mechanism to dynamically adjust the step size, avoiding the local optimal stagnation problem caused by a fixed step size and making the optimization more precise. It also uses an optimal solution replacement mechanism to automatically select the optimal solution within the solution set, improving optimization efficiency. In addition, in each iteration, multiple solution sets are optimized simultaneously to ensure that the optimization scheme in different scenarios has higher adaptability, thereby improving the accuracy and efficiency of optimizing the acoustic layout parameters.

[0061] S400: placing the speakers in the current playback interval according to the optimal position and optimal angle, and continuing to optimize the speaker placement in the order of the playback intervals until the square dance activity ends.

[0062] Specifically, the speakers are placed in the current playback interval according to the optimal position and optimal angle, that is, the speakers are placed at the optimal coordinate point according to the calculated optimal position, and the speaker angle is adjusted to point in the optimal direction to reduce the noise impact on residential areas; the same method is used to continue to optimize the speaker placement in the order of the playback intervals, that is, after entering a new playback interval, the latest meteorological data, the probability of residents being at home, and other information are obtained, and recalculated and adjusted according to the optimization algorithm, and the speaker layout is continuously optimized to ensure that the noise impact is minimized throughout the activity until the square dance activity ends. This method is based on the dynamic speaker placement optimization strategy of the optimal solution, ensuring that the square dance speakers are in the optimal state in each playback interval, thereby minimizing the impact of noise on residents and improving the intelligent management level of square dance activities.

[0063] The square dance noise data processing method provided by the embodiment of the present invention has at least the following technical effects:

[0064] By dividing the scheduled activity time period of square dancing into multiple playback intervals, the probability set of residents being at home in the noise coverage area is obtained according to the current playback interval matching; then a noise area simulation space is constructed according to the architectural characteristics of the noise coverage area and the location coordinates of several families; further utilizing the noise area simulation space, according to current meteorological data and the probability set of residents being at home, the audio movable area range is used as the optimization space, and the audio layout parameters are iteratively optimized with the goal of minimizing the overall noise impact, and the current optimal layout parameters are output, wherein the current optimal layout parameters include the optimal position and the optimal angle; finally, the audio is placed in the current playback interval according to the optimal position and the optimal angle, and the audio placement optimization is continued in the order of the playback intervals until the square dance activity ends; the above method can improve the scientificity and rationality of the square dance activity venue and audio layout setting, so that while ensuring the entertainment experience, the regional square dance noise pollution can be minimized, and intelligent and scientific urban noise management can be achieved.

[0065] Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts become known.

[0066] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A square dance noise data processing method, characterized in that: Methods include: The scheduled activity time period of square dancing is divided into multiple playback intervals, and the probability set of residents at home in the noise coverage area is obtained based on the current playback interval. Constructing a noise area simulation space according to the architectural features of the noise coverage area and the location coordinates of several households; Using the noise area simulation space, based on current meteorological data and the probability set of residents being at home, with the range of the movable area of the speakers as the optimization space, and with the goal of minimizing the overall noise impact, iteratively optimizing the speakers' layout parameters, outputting the current optimal layout parameters, wherein the current optimal layout parameters include the optimal position and the optimal angle; The speakers are placed in the current playback interval according to the optimal position and optimal angle, and the speaker placement optimization is continued in the order of the playback intervals until the square dance activity ends.

2. A square dance noise data processing method according to claim 1, characterized in that: The scheduled activity time period of square dancing is divided into multiple playback intervals. The probability set of residents at home in the noise coverage area is obtained based on the current playback interval matching, including: A noise diffusion coverage simulation is performed based on the maximum noise intensity of square dancing within a preset time period and the characteristics of surrounding buildings to determine the noise coverage area. During the noise diffusion simulation, the default regional wind speed is 0. Divide the scheduled activity time period of the square dance according to the scheduled time step and determine multiple playback intervals; Counting the historical proportions of residents at home for a number of households in the noise coverage area during the multiple playback intervals, setting them as historical resident at home probabilities, obtaining multiple sets of historical resident at home probabilities, and establishing a probability mapping relationship between the playback intervals and the sets of historical resident at home probabilities; The probability set of the resident being at home is obtained by matching the current playback interval with the probability mapping relationship.

3. A square dance noise data processing method according to claim 1, characterized in that: Using the noise area simulation space, based on current meteorological data and the probability set of residents being at home, the speaker movable area is used as the optimization space, and the overall noise impact is minimized. The optimal layout parameters are iteratively optimized, and the current optimal layout parameters are output, including: Obtaining a movable area range of a square dance speaker in the area, and randomly selecting multiple speaker placement positions within the movable area range; Arrange a plurality of speaker placement angles according to predetermined angle intervals, and randomly combine the plurality of speaker placement angles and a plurality of speaker placement positions to generate a plurality of initial layout parameters; By utilizing the noise area simulation space, according to current meteorological data and the probability set of residents being at home, and with the goal of minimizing the overall noise impact, an iterative optimization of the sound layout parameters is performed based on the several initial layout parameters, and the current optimal layout parameters are output.

4. A square dance noise data processing method according to claim 3, characterized in that: Using the noise area simulation space, based on current meteorological data and the probability set of residents being at home, with the goal of minimizing the overall noise impact, an iterative optimization of the sound layout parameters is performed based on the several initial layout parameters, and the current optimal layout parameters are output, including: Using the noise region simulation space and combining it with a BP neural network to construct a noise diffusion simulator; Monitor and obtain current meteorological data, including wind speed and direction; Inputting the current meteorological data and a plurality of initial layout parameters into the noise diffusion simulator, and outputting a plurality of noise simulation intensity sets, wherein each noise simulation intensity set includes a plurality of noise simulation intensities of a plurality of households; Calculating a plurality of overall impact coefficients based on the plurality of noise simulation intensity sets and the plurality of resident at-home probability sets; Iterative optimization of the acoustic layout parameters is performed according to the plurality of initial layout parameters and the plurality of overall influence coefficients, and the current optimal layout parameters are output.

5. A square dance noise data processing method according to claim 4, characterized in that: The noise region simulation space is used in combination with a BP neural network to construct a noise diffusion simulator, including: Based on the noise region simulation space, an initial noise diffusion simulator is constructed using a BP neural network as a prediction logic; Based on regional noise monitoring records, sample meteorological data sets and sample sound layout parameter sets are collected, and noise intensities of several households under different sample meteorological data and sample sound layout parameters are counted to obtain multiple sample noise intensity sets; The sample meteorological data set and the sample acoustic layout parameter set are used as inputs, and the multiple sample noise intensity sets are used as supervision to train the initial noise diffusion simulator until convergence, thereby obtaining the noise diffusion simulator.

6. A square dance noise data processing method according to claim 4, characterized in that: A plurality of overall impact coefficients are calculated based on the plurality of noise simulation intensity sets and the plurality of resident at-home probability sets, including: Establish a location mapping relationship between the probability of residents being at home and the noise simulation intensity; Based on the location mapping association, taking the probability of residents being at home as a weight, weighted calculations are performed on the several noise simulation intensity sets according to the resident at home probability sets, and several overall impact coefficients are output.

7. A square dance noise data processing method according to claim 4, characterized in that: Iteratively optimizing the acoustic layout parameters according to the plurality of initial layout parameters and the plurality of overall influence coefficients, and outputting the current optimal layout parameters, includes: Based on the plurality of overall influence coefficients, mapping and arranging the plurality of initial layout parameters according to the coefficients from small to large to generate an initial parameter sequence; Set the initial parameters as the initial solution, and set the first N initial solutions of the initial parameter sequence as the optimal solution, and the last M initial solutions as the inferior solution, where M is K times N, and K is an integer greater than 10 and less than 50; Randomly cluster the M inferior solutions with N optimal solutions as the center, generate N solution sets, and calculate N adjustment step size sets; Within the N solution sets, optimizing and adjusting the inferior solutions in the solution set according to the N adjustment step sets, with the superior solution as the adjustment direction, and outputting N updated solution sets, wherein if the inferior solution after adjustment exceeds the range of the movable area of the speaker, no adjustment is performed; Calculating the overall influence coefficient of the inferior solution in the N updated solution sets, and identifying the N updated solution sets. If the overall influence coefficient of the inferior solution is less than the overall influence coefficient of the superior solution in the same updated solution set, then replacing the superior solution with the inferior solution; Continue iterating and optimizing until the predetermined number of optimizations is reached, output N current updated solution sets, select the current updated solution set with the smallest sum of the overall influence coefficients as the optimal solution set, and set the optimal solution of the optimal solution set as the current optimal layout parameter.

8. A square dance noise data processing method according to claim 7, characterized in that: The N adjustment step size sets are calculated, including: Configure the initial adjustment step, where the initial adjustment step includes the position adjustment amplitude and the angle adjustment amplitude; Randomly selecting a first solution set, and obtaining a first optimal solution overall influence coefficient of a first optimal solution in the first solution set, and multiple first inferior solution overall influence coefficients of multiple inferior solutions; Calculating deviation ratios between the first inferior solution overall influence coefficients and the first superior solution overall influence coefficient respectively, and setting a plurality of first correction weights according to the deviation ratios, wherein the deviation ratios and the correction weights are positively correlated; The initial adjustment step length is optimized and corrected according to the multiple first correction weights to obtain a first adjustment step length set, which is added to the N adjustment step length sets.

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