A control system and method for a stage intelligent sound control audio

Abstract

The present invention relates to the technical field of remote volume adjustment, and specifically to a control system and method for a stage intelligent sound control audio system. In the present invention, by collecting environmental noise data in real time and combining dynamic data analysis methods, it is possible to accurately capture the time distribution and amplitude characteristics of volume fluctuations, and predict future volume trends based on historical data. This logic based on dynamic prediction enhances the response ability to sound changes in volume control. The Kriging interpolation method is used to accurately analyze the sound field requirements, and by adjusting the sound field parameters to match the requirements of different regions, the sound field uniformity and coverage accuracy are improved. At the same time, the intelligence and automation level of the audio system are improved. The output power and frequency distribution of audio equipment are dynamically adjusted through the particle swarm optimization algorithm, so that the output volume can be continuously optimized according to the real-time environment and sound field requirements, meeting the personalized needs of different stage performances.

Description

Technical Field

[0001] The present invention relates to the technical field of remote volume adjustment, and particularly to a control system and method for a stage intelligent sound control audio system. Background Art

[0002] The technical field of remote volume adjustment focuses on the combination of wireless communication, control algorithms, and audio equipment to achieve remote adjustment and optimization of the volume of audio devices, aiming to meet the flexible control requirements for audio output in various application scenarios.

[0003] The purpose of the control system of the stage intelligent sound control audio is to enhance the intelligent level and remote control ability of the stage audio system, meet the real-time adjustment requirements for sound in different performance scenarios, achieve rapid and accurate adjustment of the audio volume, ensure the acoustic effect of the stage performance, and improve the overall performance quality.

[0004] Although the existing technology can achieve remote volume adjustment, it lacks real-time monitoring and prediction of environmental volume fluctuation data, resulting in the system being unable to respond promptly to sudden volume changes. The output adjustment of audio equipment relies on single parameter adjustment, making it difficult to accurately match the sound field requirements in different areas, which may lead to situations where the sound pressure is too high or the coverage is insufficient in some areas, reducing the overall sound field uniformity. Lacking analysis of the sound field layout and audience distribution, the existing system is difficult to adjust according to the actual acoustic requirements of the venue, which is likely to cause sound distribution imbalance in the changing stage scene, affecting the auditory experience of the audience, limiting the adaptability of the existing system in complex scenarios, unable to meet diverse stage requirements, and affecting the overall sound effect quality of the stage performance. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and a control system and method for a stage intelligent sound control audio are proposed.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: A control system for a stage intelligent sound control audio includes:

[0007] Real-time volume monitoring module: Collect volume fluctuation data through an environmental noise sensor, monitor the volume data in real time, predict the future volume trend through dynamic data analysis, collect changes in environmental volume and conduct volume fluctuation analysis to obtain environmental volume prediction;

[0008] Volume adjustment strategy module: Based on the environmental volume prediction, adopt the Kriging interpolation method, analyze the sound field requirements in combination with the venue layout information and audience distribution data, simulate the influence of different sound field arrangements by adjusting the volume parameters, adjust the volume output parameters to match the ideal sound field distribution, and generate adjustment strategy parameters;

[0009] Dynamic Output Adjustment Module: Based on the adjustment strategy parameters, the particle swarm optimization algorithm is used to adjust the output settings of each audio device. By continuously monitoring and adjusting, the sound field effect is matched to the venue sound field distribution, and the audio device output is adjusted in real time to obtain the dynamic adjustment result;

[0010] Volume Fine Adjustment Module: Based on the dynamic adjustment result, the volume is optimized by adjusting it within a small range to balance the sound field, and the volume output is precisely adjusted to generate the fine adjustment result;

[0011] Volume Effect Evaluation Module: Based on the fine adjustment result, the sound level meter is used to continuously monitor the volume output and the sound field effect while evaluating the impact of volume adjustment on the listener experience and analyzing the sound field uniformity to obtain the adjustment effect evaluation.

[0012] Preferably, the real-time volume monitoring module includes a volume data acquisition sub-module, a dynamic data analysis sub-module, and a volume trend prediction sub-module, where:

[0013] Volume Data Acquisition Sub-module: Based on the ambient noise sensor, the volume fluctuation data is collected. By time-sharing sampling, the amplitude and frequency changes of the volume fluctuation are recorded, and the volume fluctuation data is marked on the time axis and segmented and sorted to generate the volume fluctuation acquisition data;

[0014] Dynamic Data Analysis Sub-module: Based on the volume fluctuation acquisition data, the volume change points and statistical data distribution within a continuous time interval are extracted, and the volume fluctuation frequency and amplitude characteristics are analyzed to obtain the volume fluctuation law analysis result;

[0015] Volume Trend Prediction Sub-module: Based on the volume fluctuation law analysis result, the volume historical fluctuation data is analyzed and the volume trend curve is calculated to predict the volume change amplitude in the future time interval to obtain the ambient volume prediction.

[0016] Preferably, the volume adjustment strategy module includes a sound field demand analysis sub-module, a volume parameter adjustment simulation sub-module, and an adjustment strategy parameter generation sub-module, where:

[0017] Sound Field Demand Analysis Sub-module: Based on the ambient volume prediction, the Kriging interpolation method is used. Combining the venue layout information and the listener distribution data, by calculating the sound field distribution data at different positions within the venue, the sound field adjustment demand data for specific areas is extracted to obtain the sound field demand parameters;

[0018] Volume Parameter Adjustment Simulation Sub-module: Based on the sound field demand parameters, the volume parameter adjustment simulation is carried out. By adjusting the output power and frequency distribution of multiple audio devices within the venue, the specific impact of volume change on the sound field layout is simulated, and the simulated sound field distribution map is generated to obtain the volume parameter simulation data;

[0019] Adjustment strategy parameter generation sub-module: Based on the volume parameter simulation data, generate adjustment strategy parameters. By analyzing the coverage parameters and output status of audio devices in the region of the sound field distribution map, determine the output configuration and adjustment order of each device, and obtain the adjustment strategy parameters.

[0020] Preferably, the Kriging interpolation method is in accordance with the formula:

[0021]

[0022] Where: Z ′ (s) is the sound field distribution value at the position s to be predicted, μ is the global average sound field value, λ i is the weight coefficient, calculated through the covariance matrix, Z(s i ) is the sound field value of the sampling point s i n is the total number of sampling points, α is the adjustment coefficient related to the site layout, D(s, L) is the distance influence function from the prediction point s to the obstacle, L is the set of obstacle distributions in the site, β is the adjustment coefficient related to the listener distribution density, H(s) is the listener distribution density value at the prediction point s, γ is the adjustment coefficient related to the sound source power, and P(s) is the sound source power value received at the prediction point s.

[0023] Preferably, the dynamic output adjustment module includes an output parameter adjustment sub-module, a sound field effect monitoring sub-module, and a dynamic adjustment result generation sub-module, where:

[0024] Output parameter adjustment sub-module: Based on the adjustment strategy parameters, use the particle swarm optimization algorithm to dynamically adjust the power distribution range of the audio devices, adjust the spatial direction and coverage range of the sound output and associate with the sound field distribution, complete the coordination of output parameters, and generate the output adjustment parameters of the audio devices;

[0025] Sound field effect monitoring sub-module: Based on the output adjustment parameters of the audio devices, set multiple monitoring points to record the changes in sound pressure level and volume coverage range, and record the sound wave distribution data in the sound field in real time, perform sound field coverage and uniformity verification, and obtain the dynamic monitoring data of the sound field effect;

[0026] Dynamic adjustment result generation sub-module: Based on the dynamic monitoring data of the sound field effect, perform step-by-step adjustment of the output parameters of the audio devices. By analyzing the differences in the sound field coverage range and the uneven sound pressure distribution areas, correct the output configuration to obtain the dynamic adjustment result.

[0027] Preferably, the particle swarm optimization algorithm is in accordance with the formula:

[0028]

[0029] Where: is the velocity value of particle j at the (k + 1)-th iteration, φ is the inertia weight, is the velocity value of particle j at the k-th iteration, a1 is the learning factor, b1 is a random number, q j is the adaptation position reached by particle j in historical iterations, is the position of particle j at the k-th iteration, a2 is the learning factor, b2 is a random number, z is the global adaptation position in the history of all particles, ρ is the sound field coverage weight factor, c j is the dynamic environment correction coefficient, is the current position of the particle is the deviation value from the target sound field coverage area, η is the sound directivity weight factor, d j is the sound output adjustment correction coefficient, is the current position of the particle is the optimized value of the sound source directivity corresponding to the current position of the particle.

[0030] Preferably, the volume fine adjustment module includes a volume fine-tuning sub-module, a sound field balance optimization sub-module, and a fine adjustment result generation sub-module, where:

[0031] Volume fine-tuning sub-module: Based on the dynamic adjustment result, by adjusting the output volume intensity and frequency distribution value of the audio device in a divided area, setting the relative power ratio between devices, and verifying the sound field coverage of the local area, generate volume fine-tuning optimization parameters;

[0032] Sound field balance optimization sub-module: Based on the volume fine-tuning optimization parameters, by measuring the sound pressure level differences in each area and optimizing the volume distribution in sequence, compare and adjust the strong and weak relationships of the sound field in the coverage area, and obtain sound field balance optimization data;

[0033] Fine adjustment result generation sub-module: Based on the sound field balance optimization data, by optimizing the output frequency, power, and coverage angle of the audio device in a divided area, verify and correct the uniformity of the global sound field distribution, and generate a fine adjustment result.

[0034] Preferably, the volume effect evaluation module includes a volume output monitoring sub-module, an audience experience evaluation sub-module, and a sound field uniformity analysis sub-module, where:

[0035] Volume output monitoring sub-module: Based on the fine adjustment result, use a sound level meter to collect volume data at fixed points in different areas, record the sound pressure level and volume fluctuation range in each area at the same time, organize the collected sound pressure data and mark the time axis, and generate volume output monitoring data;

[0036] Audience experience evaluation sub-module: Based on the monitored volume output data, by collecting feedback data from audiences in different areas and analyzing the auditory comfort in combination with the sound pressure level changes, classifying and statistically analyzing the feedback data by region, obtaining the auditory perception distribution of each area, and obtaining the audience experience evaluation data;

[0037] Sound field uniformity analysis sub-module: Based on the audience experience evaluation data, by comparing the sound pressure levels of each area to calculate the distribution uniformity of the sound field, recording the areas with large sound pressure level differences, and summarizing the specific values and distribution characteristics of the sound pressure differences between regions, obtaining the adjustment effect evaluation.

[0038] Preferably, for collecting feedback data from audiences in different areas and analyzing the auditory comfort in combination with the sound pressure level changes, based on the sound pressure level changes and audience feedback data in different areas, by setting fixed-point collection feedback within the area, recording the subjective evaluations of the audience on volume comfort, auditory fatigue, and sound clarity, organizing the audience feedback data into three dimensions: scoring data, perception description, and area distribution data, and at the same time combining the data trend of the sound pressure level changes, performing interval matching and classification statistics between the fluctuation range of the sound pressure level and the comfort score of the audience, analyzing the influence relationship between the sound pressure level changes and the auditory comfort, and summarizing to obtain the auditory perception distribution.

[0039] A control method for a stage intelligent sound control audio system, the control method of the stage intelligent sound control audio system is executed based on the above-mentioned control system of the stage intelligent sound control audio system, and includes the following steps:

[0040] Step 1: Based on the real-time volume data obtained by the environmental noise sensor, segment the volume change curve for each time period, calculate the volume fluctuation amplitude, extract the peak points and trough points of the fluctuation, analyze the fluctuation frequency and amplitude change, compare the change trend with the established standard threshold, and generate an environmental volume prediction model through multi-segment interval calculation;

[0041] Step 2: Based on the environmental volume prediction model, call the venue layout information and audience distribution data, extract the audience distribution density and spatial parameters from the sound equipment in each sound field area, calculate the sound pressure coverage range within the area, compare the sound pressure coverage values of each area with the venue sound field requirements, extract the insufficient coverage parameters and deduce the adjustment range, combine the sound field reflection characteristics of the venue and the target volume distribution parameters, adjust the regional sound pressure matching value, and use the Kriging interpolation algorithm to fit and supplement the multi-regional sound field data to generate the volume adjustment strategy parameters;

[0042] Step 3: Based on the volume adjustment strategy parameters, dynamically adjust the output settings of the audio equipment. Calculate the corresponding values of the output power of each device and the frequency band range. By matching the device power with the partition sound pressure coverage requirements one by one, regulate the output power of the devices in different partitions, gradually allocate the volume output parameters to each device, and use the particle swarm optimization algorithm to optimize the allocation of the device output configuration to complete the dynamic output adjustment and generate the dynamic output configuration parameters of the audio equipment;

[0043] Step 4: Based on the dynamic output configuration parameters of the audio equipment, make small-range adjustments to the sound pressure distribution values within each partition. Compare the average sound pressure within the partition with the target sound field distribution values one by one, calculate the fine-tuning parameter range, and finally complete the compensation of the sound pressure value and the optimization of the device output by optimizing the power output of each device and the frequency divider frequency band settings, and generate the volume fine optimization result;

[0044] Step 5: Based on the volume fine optimization result, monitor the real-time volume value output by the audio equipment through a sound level meter, extract the sound pressure distribution and uniformity parameters within each partition sound field, compare the sound pressure distribution data with the optimization result parameters, calculate the sound field uniformity deviation and the distribution of the volume change perceived by the audience, evaluate the overall volume adjustment effect, and finally generate the sound field adjustment evaluation data.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0046] 1. In the present invention, by collecting real-time environmental noise data and combining dynamic data analysis methods, it is possible to accurately capture the time distribution and amplitude characteristics of volume fluctuations, and predict future volume trends based on historical data. This logic based on dynamic prediction enhances the response ability to sound changes in volume control;

[0047] 2. In the present invention, the Kriging interpolation method is used to accurately analyze the sound field requirements. By adjusting the sound field parameters to match the requirements of different regions, the sound field uniformity and coverage accuracy are improved, and at the same time, the intelligence and automation level of the audio system are improved;

[0048] 3. In the present invention, the particle swarm optimization algorithm is used to dynamically adjust the output power and frequency distribution of the audio equipment, so that the output volume can be continuously optimized according to the real-time environment and sound field requirements, reducing the phenomenon of uneven sound field distribution. By combining continuous sound pressure level monitoring and listener feedback data, the adaptability to complex scenarios of the audio system is improved, meeting the personalized needs of different stage performances. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is the system flow chart of the present invention;

[0050] Figure 2Schematic diagram of the system framework of the present invention;

[0051] Figure 3 Schematic diagram of the method steps of the present invention. Detailed implementation manners

[0052] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] Please refer to Figure 1 , the present invention provides a technical solution: A control system for a stage intelligent sound control audio includes:

[0054] Real-time volume monitoring module: Collect volume fluctuation data through an environmental noise sensor, monitor the volume data in real time, predict the future volume trend through dynamic data analysis, collect changes in environmental volume and perform volume fluctuation analysis to obtain an environmental volume prediction;

[0055] Volume adjustment strategy module: Based on the environmental volume prediction, use Kriging interpolation method, combine the venue layout information and the audience distribution data to analyze the sound field requirements, adjust the volume output parameters by simulating the influence of different sound field arrangements, and adjust the volume output parameters to match the ideal sound field distribution to generate adjustment strategy parameters;

[0056] Dynamic output adjustment module: Through the adjustment strategy parameters, use the particle swarm optimization algorithm to adjust the output settings of each audio device, continuously monitor and adjust the sound field effect to match the venue sound field distribution, and adjust the audio device output in real time to obtain dynamic adjustment results;

[0057] Volume fine adjustment module: Based on the dynamic adjustment results, optimize the volume matching by adjusting the volume in a small range, accurately adjust the volume output to balance the sound field, and generate fine adjustment results;

[0058] Volume effect evaluation module: Based on the fine adjustment results, continuously monitor the volume output and the sound field effect through a sound level meter, evaluate the impact of volume adjustment on the audience experience and analyze the sound field uniformity to obtain an adjustment effect evaluation.

[0059] Please refer to Figure 2 , the real-time volume monitoring module includes a volume data collection sub-module, a dynamic data analysis sub-module and a volume trend prediction sub-module, where:

[0060] Volume data collection sub-module: Based on the environmental noise sensor, collect volume fluctuation data, record the amplitude and frequency changes of volume fluctuations through time-sharing sampling, and mark and segment the volume fluctuation data on the time axis to generate volume fluctuation collection data;

[0061] Dynamic data analysis sub-module: Collect data based on volume fluctuations, extract volume change points and statistical data distributions within continuous time intervals, analyze the frequency and amplitude characteristics of volume fluctuations, and obtain the analysis results of volume fluctuation patterns;

[0062] Volume trend prediction sub-module: Based on the analysis results of volume fluctuation patterns, analyze the historical volume fluctuation data and calculate the volume trend curve, predict the volume change amplitude in the future time interval, and obtain the environmental volume prediction;

[0063] Volume data collection sub-module: Based on the volume fluctuation data collected by the environmental noise sensor, use the fixed time interval sampling method to record the sampling data at different times, set the sampling interval to 500 milliseconds and record the sampled volume values, attach a time stamp label to the volume value of each sampling point through the time stamp annotation method, and divide the sampling data into independent data segments according to the time interval through segmented processing. The time interval division rule is one segment per 60 seconds and each segment of data is stored as an array structure of the volume fluctuation values within the segment, generating the volume fluctuation collection data;

[0064] Dynamic data analysis sub-module: Based on the volume fluctuation collection data, use the linear regression algorithm to statistically analyze the volume change points within a continuous time interval, input the time stamp and volume value of the sampling point, take the time stamp as the independent variable and the volume value as the dependent variable, calculate the volume fluctuation trend within the time interval and extract the extreme value points and mean value points of the fluctuation, use the discrete Fourier transform to calculate the frequency distribution of the volume value sequence and extract the frequency distribution sequence and amplitude distribution characteristics, analyze the frequency range and amplitude distribution law of volume fluctuations, and generate the analysis results of volume fluctuation patterns;

[0065] Volume trend prediction sub-module: Based on the analysis results of volume fluctuation patterns, use the ARIMA model to perform trend fitting analysis on the historical volume fluctuation data, input the volume fluctuation data of the past 120 seconds into the model and set the autoregressive order of the model to 2, the difference order to 1, and the moving average order to 2, generate time series prediction data using the model training results and perform normalization processing, calculate the volume change amplitude in the future time interval through the multi-time step prediction method, set the prediction time interval to the next 10 minutes and the step size to 30 seconds, and generate the environmental volume prediction.

[0066] Please refer to Figure 2 , the volume adjustment strategy module includes a sound field demand analysis sub-module, a volume parameter adjustment simulation sub-module, and an adjustment strategy parameter generation sub-module, where:

[0067] Sound field demand analysis sub-module: Based on the environmental volume prediction, use the Kriging interpolation method, combine the venue layout information and the audience distribution data, calculate the sound field distribution data at different positions within the venue through calculation, extract the sound field adjustment demand data for specific areas, and obtain the sound field demand parameters;

[0068] Volume parameter adjustment simulation sub-module: Based on the sound field requirement parameters, it conducts volume parameter adjustment simulation. By adjusting the output power and frequency distribution of multiple audio devices in the venue, it simulates the specific impact of volume changes on the sound field layout, generates a simulated sound field distribution map, and obtains volume parameter simulation data;

[0069] Adjustment strategy parameter generation sub-module: Based on the volume parameter simulation data, it generates adjustment strategy parameters. By analyzing the coverage parameters and output status of audio devices in the area of the sound field distribution map, it determines the output configuration and adjustment sequence of each device, and obtains adjustment strategy parameters;

[0070] Sound field requirement analysis sub-module: Based on the ambient volume prediction, it uses the Kriging interpolation method to calculate the venue layout information and audience distribution data. First, it samples the spatial coordinate points in the venue layout information, sets the sampling interval to 1 meter, and constructs the venue coordinate points into a three-dimensional space lattice. Based on the audience distribution data, it extracts the number of audiences and distribution density values in each area, associates the coordinate points with the density data, and inputs them into the Kriging model. By calculating the covariance matrix between spatial points, it determines the interpolation kernel function, uses the covariance matrix and the interpolation kernel function to perform interpolation calculations on the sound field distribution data at each position in the sound field, calculates the difference between the interpolation result and the original distribution data, extracts the sound field adjustment requirement data for specific areas in the venue, and generates sound field requirement parameters;

[0071] Volume parameter adjustment simulation sub-module: Based on the sound field requirement parameters, it adjusts and simulates the output power and frequency distribution of multiple audio devices in the venue. It calls the audio device parameter list to extract the output power value, frequency range, and coverage area information of each device, uses the power adjustment algorithm to gradually adjust the output power value of the device, sets the increment of each adjustment to 20% of the current power value and records the power change curve, combines the frequency range to adjust the frequency divider settings of each device, moves the low-frequency and high-frequency cut-off points of the frequency divider 10 Hz to both sides to change the frequency distribution range, and at the same time calls the adjusted power and frequency parameters to generate a simulated sound field distribution map, records the sound field coverage range and sound pressure changes one by one, and generates volume parameter simulation data;

[0072] Adjustment strategy parameter generation sub-module: Based on the volume parameter simulation data, by analyzing the sound pressure coverage status of each area in the simulated sound field distribution map and the output configuration of the audio devices, it extracts the output power and coverage range of each device, uses the sorting algorithm to calculate the adjustment sequence according to the distance of the devices from near to far, gradually distributes the output power of the devices according to the adjustment strategy parameters and sets the power increment, generates the device configuration list in the adjustment strategy, and obtains the adjustment strategy parameters by integrating the output power and frequency range of the devices.

[0073] Please refer to Figure 2, the Kriging interpolation method, according to the formula:

[0074]

[0075] Where: Z ′ (s) is the sound field distribution value at the position s to be predicted, μ is the global average sound field value, λ i is the weight coefficient, calculated through the covariance matrix, Z(s i ) is the sound field value of the sampling point s i , n is the total number of sampling points, α is the adjustment coefficient related to the site layout, D(s, L) is the distance influence function from the prediction point s to the obstacle, L is the set of obstacle distributions in the site, β is the adjustment coefficient related to the listener distribution density, H(s) is the listener distribution density value at the prediction point s, γ is the adjustment coefficient related to the sound source power, and P(s) is the sound source power value received at the prediction point s.

[0076] Execution process: First, calculate the global average sound field value μ, establish the covariance matrix through the sound field values Z(s i ) of the sampling points, determine the weight coefficient λ according to the spatial correlation i , calculate the preliminarily predicted sound field value, then optimize the sound field distribution using the new parameters, analyze the site layout information to obtain the set of obstacle distributions L, calculate the distance D(s, L) from the prediction point s to the nearest obstacle through geometric acoustic simulation, and set the adjustment coefficient α in combination with the material characteristics of the obstacle. Then, combine the listener distribution data to statistically obtain the listener density value H(s) at the prediction point s, determine the adjustment coefficient β according to the attenuation effect of the density change on the sound field. Finally, analyze the sound source power distribution, measure the sound source power value P(s) received at the prediction point s, optimize the adjustment coefficient γ according to the influence of the power on the sound field, substitute all parameters into the formula, realize the accurate prediction of the stage sound field, and provide the sound field distribution basis for the intelligent control of the audio system.

[0077] Please refer to Figure 2 , the dynamic output adjustment module includes an output parameter adjustment sub-module, a sound field effect monitoring sub-module, and a dynamic adjustment result generation sub-module, where:

[0078] Output parameter adjustment sub-module: Based on the adjustment strategy parameters, use the particle swarm optimization algorithm to dynamically adjust the power distribution range of the audio equipment, adjust the spatial direction and coverage range of the sound output and associate with the sound field distribution, complete the coordination of the output parameters, and generate the output adjustment parameters of the audio equipment;

[0079] Sound field effect monitoring sub-module: Based on the output adjustment parameters of the audio equipment, set multiple monitoring points to record the changes in the sound pressure level and volume coverage range, and record the sound wave distribution data in the sound field in real time, perform sound field coverage and uniformity verification, and obtain the dynamic monitoring data of the sound field effect;

[0080] Dynamic adjustment result generation sub-module: Based on the dynamic monitoring data of the sound field effect, gradually adjust the output parameters of the audio equipment. By analyzing the differences in the sound field coverage range and the uneven sound pressure distribution areas, correct the output configuration to obtain the dynamic adjustment result;

[0081] Output parameter adjustment sub-module: Based on the adjustment strategy parameters, use the particle swarm optimization algorithm to dynamically adjust the power distribution range of the audio equipment. Set the output power value, sound wave direction angle, and coverage range of the audio equipment as the particle positions. Set the initial velocity of each particle to 5% of the power adjustment range. Use the global fitness function to evaluate the sound field coverage range and power distribution of each device. The fitness function generates a fitness value by calculating the coverage uniformity and sound pressure distribution range of the target sound field area. Adjust the particle positions to the optimal solutions of the power value and sound wave direction angle through the update formula, and iterate until convergence to complete the output power distribution and direction angle adjustment of the audio equipment, generating the output adjustment parameters of the audio equipment;

[0082] Sound field effect monitoring sub-module: Based on the output adjustment parameters of the audio equipment, monitor the sound pressure level and volume coverage range in the sound field in real time. Call the sound field monitoring module to set the monitoring point coordinates and sampling frequency. The monitoring point coordinates are evenly distributed at 5-meter intervals and cover all the audience areas of the venue. The sampling frequency is set to collect once every 1 second. Record the sound pressure level data and coverage range values of each monitoring point. Store the collected data with a real-time timestamp as a dynamic distribution data table. Calculate the coverage uniformity value by comparing the sound pressure differences between the monitoring points, and verify whether the sound pressure levels in each area meet the distribution requirements to generate the dynamic monitoring data of the sound field effect;

[0083] Dynamic adjustment result generation sub-module: Based on the dynamic monitoring data of the sound field effect, gradually adjust the output parameters of the audio equipment. By analyzing the difference values of the sound pressure level and coverage range in each area, extract the area data with uneven sound pressure distribution. Associate the area data with the current output parameters of the audio equipment, and gradually adjust the output power and direction angle of the equipment in the uneven area. The step size of the power adjustment is 10% of the current value and record the adjusted coverage range to complete the correction of the equipment configuration and generate the dynamic adjustment result;

[0084] Please refer to Figure 2 , the particle swarm optimization algorithm, according to the formula:

[0085]

[0086] Where: is the velocity value of particle j at the (k + 1)-th iteration, φ is the inertia weight, is the velocity value of particle j at the k-th iteration, a1 is the learning factor, b1 is a random number, qj is the adapted position reached by particle j in historical iterations, is the position of particle j at the k-th iteration, a2 is the learning factor, b2 is a random number, z is the global adapted position in the history of all particles, ρ is the sound field coverage weight factor, c j is the dynamic environment correction coefficient, is the current position of the particle is the deviation value from the target sound field coverage area, η is the sound directivity weight factor, d j is the sound output adjustment correction coefficient, is the current position of the particle is the optimized value of the sound source directivity corresponding to the current position of the particle.

[0087] Execution process: First, initialize the velocity and position of the particle swarm, and calculate the current velocity of the particle and position Balance the global search and local search capabilities of the particle swarm through the inertia weight υ. At the same time, based on the learning factors a1 and a2 combined with the random numbers b1 and b2, guide the particle to move towards its individual historical optimal position q j and the global historical optimal position z, complete the basic update of the particle velocity. Then, for the specific requirements of sound control, introduce new parameters. First, by analyzing the sound field distribution and coverage requirements of the site, calculate the sound field coverage weight factor ρ, and combine the current position of the particle with the geometric deviation from the target coverage area to obtain the coverage deviation value dynamically adjust the coverage range, and then calculate the dynamic environment correction coefficient c by combining real-time environment detection data j , correct the particle position and velocity, and analyze the directivity requirements of the sound equipment. Combine the coverage angle optimization strategy of the sound source to calculate the sound directivity weight factor η and the directivity optimization value Achieve uniform distribution of the sound field by optimizing the sound field coverage direction. Finally, calculate the adjustment correction coefficient d based on the output power adjustment strategy of the sound equipment j , comprehensively consider the power distribution range and directivity optimization, substitute all parameters into the formula, complete the update of the particle velocity and position, generate the output adjustment parameters of the sound equipment through multiple iterations, and realize the intelligent coordination of power distribution, direction coverage and sound field distribution.

[0088] Please refer to Figure 2 , the volume fine adjustment module includes a volume fine tuning sub-module, a sound field balance optimization sub-module and a fine adjustment result generation sub-module, where:

[0089] Volume fine tuning sub-module: Based on the dynamic adjustment result, adjust the output volume intensity and frequency distribution value of the sound equipment by region, set the relative power ratio between the devices and verify the local area sound field coverage, and generate volume fine tuning optimization parameters;

[0090] Sound field balance optimization sub-module: Based on the volume fine-tuning optimization parameters, by measuring the sound pressure level differences in each area and optimizing the volume distribution in sequence, compare and adjust the sound field strength relationship in the coverage area to obtain sound field balance optimization data;

[0091] Fine adjustment result generation sub-module: Based on the sound field balance optimization data, by optimizing the output frequency, power, and coverage angle of the audio equipment in each area, check and correct the balance of the global sound field distribution to generate fine adjustment results;

[0092] Volume fine-tuning sub-module: Based on the dynamic adjustment results, by adjusting the output volume intensity and frequency distribution values of the audio equipment in each area, call the device parameter management module to extract the current power output value and frequency range of each audio equipment, calculate the difference between the sound pressure level data and the target volume value in each area, use the difference as the volume adjustment step size, set the step size of the volume intensity adjustment to 10% of the difference and increase it gradually, calculate the relative power ratio between devices using the ratio control method, use the regional sound field coverage model to check the sound field coverage range of the local area point by point, compare the sound pressure value of each check point with the sound pressure value of the adjacent point and record the power configuration parameters of the coverage abnormal points to generate volume fine-tuning optimization parameters;

[0093] Sound field balance optimization sub-module: Based on the volume fine-tuning optimization parameters, measure the sound pressure level data in each area, gradually optimize the sound pressure level difference using the hierarchical optimization method, divide the sound pressure level data in the area into five levels according to the strength relationship, dynamically adjust the output volume of the audio equipment in each level, call the regional sound field data model to calculate the difference in the sound field strength relationship in the coverage area, control the strength difference within the set threshold range and make local adjustments to the uneven areas, and perform a secondary comparison on the corrected parameters by updating the device frequency output range and power coverage value to obtain sound field balance optimization data;

[0094] Fine adjustment result generation sub-module: Based on the sound field balance optimization data, optimize the output frequency, power, and coverage angle of the audio equipment in each area, call the global sound field balance model to calculate the sound field coverage state in the current area, gradually adjust the upper and lower limits of the frequency band according to the coverage range requirements for the frequency output value, set the adjustment amplitude each time to 1% of the current frequency band and record the sound field coverage value after adjustment, correct the sound pressure distribution by adjusting the coverage angle in each area, set the angle adjustment step size to 1 degree and dynamically record the corresponding sound pressure level change value after the angle change, check and correct the parameter configuration point by point for the global sound field balance to generate fine adjustment results.

[0095] Please refer to Figure 2, the volume effect evaluation module includes a volume output monitoring sub-module, an audience experience evaluation sub-module, and a sound field uniformity analysis sub-module, where:

[0096] Volume output monitoring sub-module: Based on the fine adjustment results, use a sound level meter to collect volume data at fixed points in different areas, and at the same time record the sound pressure level and volume fluctuation range of each area, organize the collected sound pressure data and mark the time axis to generate volume output monitoring data;

[0097] Audience experience evaluation sub-module: Based on the volume output monitoring data, collect feedback data from audiences in different areas and analyze the auditory comfort by combining the sound pressure level changes, classify and statistically analyze the feedback data by area to obtain the auditory perception distribution of each area and obtain the audience experience evaluation data;

[0098] Sound field uniformity analysis sub-module: Based on the audience experience evaluation data, calculate the distribution uniformity of the sound field by comparing the sound pressure levels of each area, record the areas with large sound pressure level differences, and obtain the adjustment effect evaluation by summarizing the specific values and distribution characteristics of the sound pressure differences between areas;

[0099] Volume output monitoring sub-module: Based on the fine adjustment results, use a sound level meter to collect volume data at fixed points in each area, set the spatial distribution interval of the monitoring points to 5 meters and mark the coordinates by area, bind the sound pressure level data recorded at each monitoring point with the timestamp, organize the collected sound pressure data into a time series data set, segment the collected time series by 10 seconds per segment using the data segmentation algorithm, and mark the volume fluctuation range and the sound pressure level change trend within each segment to generate volume output monitoring data;

[0100] Audience experience evaluation sub-module: Based on the volume output monitoring data, collect the subjective feedback data of the audiences in each area through the area feedback collection device, divide the feedback data into three categories: volume comfort score, volume uniformity perception, and auditory fatigue degree, classify the feedback data by area and establish an area feedback data table, perform a linear correlation analysis between the subjective feedback score and the sound pressure level change by combining the sound pressure level change sequence in the monitoring data, extract the feedback mean value and distribution characteristics within the sound pressure change range of each area, and classify and summarize the feedback data by area statistical methods to obtain the audience experience evaluation data;

[0101] Sound field uniformity analysis sub-module: Based on the evaluation data of the audience experience, conduct a point-by-point comparison and analysis of the sound pressure levels in each area, map the regional sound pressure level data to the venue sound field model according to the spatial distribution of the monitoring points, obtain the non-uniformity degree of the sound field distribution by calculating the mean square deviation of the sound pressure levels between regions, record the regions with large sound pressure level differences and their corresponding specific values, analyze the distribution characteristics of the regions with sound pressure level differences and count the occurrence frequency of regional differences, generate the sound pressure distribution characteristic table of the difference regions, and obtain the evaluation of the adjustment effect;

[0102] Collect the feedback data of the audience in different areas and analyze the auditory comfort in combination with the change of the sound pressure level. Based on the change of the sound pressure level and the audience feedback data in different areas, record the subjective evaluations of the audience on the volume comfort, auditory fatigue and sound clarity by setting fixed-point collection feedback in the area. Organize the audience feedback data into three dimensions: scoring data, perception description and regional distribution data. At the same time, in combination with the data trend of the change of the sound pressure level, conduct interval matching and classification statistics between the fluctuation range of the sound pressure level and the comfort score of the audience, analyze the influence relationship between the change of the sound pressure level and the auditory comfort, and summarize to obtain the auditory perception distribution.

[0103] Please refer to Figure 3 , a control method for a stage intelligent sound control audio system. The control method of the stage intelligent sound control audio system is executed based on the above-mentioned control system of the stage intelligent sound control audio system, and includes the following steps:

[0104] Step 1: Based on the real-time volume data obtained by the environmental noise sensor, segment the volume change curve for each time period, calculate the volume fluctuation amplitude and extract the peak points and valley points of the fluctuation, analyze the fluctuation frequency and amplitude change, compare the change trend with the established standard threshold, and generate an environmental volume prediction model through multi-segment interval calculation;

[0105] Step 2: Based on the environmental volume prediction model, call the venue layout information and the audience distribution data, extract the audience distribution density and the spatial parameters from the sound equipment in each sound field area, calculate the sound pressure coverage range in the area, compare the sound pressure coverage values of each area with the venue sound field requirements, extract the insufficient coverage parameters and deduce the adjustment range, combine the sound field reflection characteristics and the target volume distribution parameters of the venue, adjust the regional sound pressure matching value, and use the Kriging interpolation algorithm to fit and supplement the parameters of the multi-region sound field data to generate the volume adjustment strategy parameters;

[0106] Step 3: Based on the volume adjustment strategy parameters, dynamically adjust the output settings of the audio equipment, calculate the corresponding values of the output power of each device and the frequency band range, control the output power of the devices in different zones by matching the device power with the zone sound pressure coverage requirements one by one, gradually distribute the volume output parameters to each device, and use the particle swarm optimization algorithm to optimize and allocate the device output configuration to complete the dynamic output adjustment and generate the dynamic output configuration parameters of the audio equipment;

[0107] Step 4: Based on the dynamic output configuration parameters of the audio equipment, make a small-range adjustment to the sound pressure distribution value within each zone, compare the average sound pressure in the zone with the target sound field distribution value one by one, calculate the range of fine-tuning parameters, and finally complete the compensation of the sound pressure value and the optimization of the device output by optimizing the power output of each device and the frequency divider band setting to generate the volume fine optimization result;

[0108] Step 5: Based on the volume fine optimization result, monitor the real-time volume value output by the audio equipment through a sound level meter, extract the sound pressure distribution and uniformity parameters in the sound field of each zone, compare the sound pressure distribution data with the optimization result parameters, calculate the sound field uniformity deviation and the distribution of the perceived volume change of the listeners, evaluate the overall volume adjustment effect, and finally generate the sound field adjustment evaluation data.

[0109] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A control system for a stage intelligent sound-controlled audio, characterized in that: The system includes: Real-time volume monitoring module: It collects volume fluctuation data through an environmental noise sensor, monitors the volume data in real time, predicts the future volume trend through dynamic data analysis, collects the changes in environmental volume and conducts volume fluctuation analysis to obtain the environmental volume prediction; Volume adjustment strategy module: Based on the environmental volume prediction, it uses Kriging interpolation method, combines the site layout information and the audience distribution data to analyze the sound field requirements, adjusts the volume output parameters by simulating the influence of different sound field arrangements through volume parameter adjustment, and generates adjustment strategy parameters by matching the ideal sound field distribution; Dynamic output adjustment module: Through the adjustment strategy parameters, it uses the particle swarm optimization algorithm to adjust the output settings of each audio device, continuously monitors and adjusts the sound field effect to match the site sound field distribution, and adjusts the output of the audio device in real time to obtain the dynamic adjustment result; Volume fine adjustment module: Based on the dynamic adjustment result, it optimizes the volume matching by adjusting the volume in a small range, precisely adjusts the volume output to balance the sound field, and generates the fine adjustment result; Volume effect evaluation module: Based on the fine adjustment result, it continuously monitors the volume output and the sound field effect through a sound level meter, evaluates the impact of volume adjustment on the audience experience and analyzes the sound field uniformity at the same time to obtain the adjustment effect evaluation.

2. The control system of the stage intelligent sound control audio according to claim 1, characterized in that, The real-time volume monitoring module includes a volume data collection sub-module, a dynamic data analysis sub-module and a volume trend prediction sub-module, where: Volume data collection sub-module: It collects volume fluctuation data based on an environmental noise sensor, records the amplitude and frequency changes of volume fluctuations through time-sharing sampling, and marks and segments the volume fluctuation data on the time axis to generate volume fluctuation collection data; Dynamic data analysis sub-module: Based on the volume fluctuation collection data, it extracts the volume change points and statistical data distribution within a continuous time interval, and analyzes the characteristics of volume fluctuation frequency and amplitude to obtain the volume fluctuation law analysis result; Volume trend prediction sub-module: Based on the volume fluctuation law analysis result, it analyzes the historical volume fluctuation data and calculates the volume trend curve, predicts the volume change amplitude in the future time interval, and obtains the environmental volume prediction.

3. The control system of the stage intelligent sound control audio according to claim 1, characterized in that, The volume adjustment strategy module includes a sound field requirement analysis sub-module, a volume parameter adjustment simulation sub-module and an adjustment strategy parameter generation sub-module, where: Sound field requirement analysis sub-module: Based on the environmental volume prediction, it uses Kriging interpolation method, combines the site layout information and the audience distribution data, calculates the sound field distribution data at different positions within the site, extracts the sound field adjustment requirement data for specific areas, and obtains the sound field requirement parameters; Volume parameter adjustment simulation sub-module: Based on the sound field requirement parameters, it conducts volume parameter adjustment simulation, adjusts the output power and frequency distribution of multiple audio devices within the site, simulates the specific influence of volume change on the sound field arrangement, and generates a simulated sound field distribution map to obtain the volume parameter simulation data; Adjustment strategy parameter generation sub-module: Based on the volume parameter simulation data, it generates adjustment strategy parameters, determines the output configuration and adjustment order of each device by analyzing the coverage parameters and output status of the audio devices in the area of the sound field distribution map, and obtains the adjustment strategy parameters.

4. The control system of the stage intelligent sound control audio according to claim 3, characterized in that The Kriging interpolation method, according to the formula: Where: Z ′ (s) is the sound field distribution value at the position s to be predicted, μ is the global average sound field value, λ i is the weight coefficient, calculated through the covariance matrix, Z(s i ) is the sound field value of the sampling point s i , n is the total number of sampling points, α is the adjustment coefficient related to the site layout, D(s, L) is the distance influence function from the prediction point s to the obstacle, L is the set of obstacle distributions in the site, β is the adjustment coefficient related to the listener distribution density, H(s) is the listener distribution density value at the prediction point s, γ is the adjustment coefficient related to the sound source power, and P(s) is the sound source power value received at the prediction point s.

5. The control system of the stage intelligent sound control audio according to claim 1, characterized in that The dynamic output adjustment module includes an output parameter adjustment sub-module, a sound field effect monitoring sub-module, and a dynamic adjustment result generation sub-module, where: Output parameter adjustment sub-module: Based on the adjustment strategy parameters, the particle swarm optimization algorithm is used to dynamically adjust the power distribution range of the audio equipment, adjust the spatial direction and coverage range of the sound output and correlate the sound field distribution, complete the coordination of output parameters, and generate the output adjustment parameters of the audio equipment; Sound field effect monitoring sub-module: Based on the output adjustment parameters of the audio equipment, multiple monitoring points are set to record the changes in sound pressure level and volume coverage range, and the sound wave distribution data in the sound field is recorded in real time to perform sound field coverage and uniformity verification, and obtain the dynamic monitoring data of the sound field effect; Dynamic adjustment result generation sub-module: Based on the dynamic monitoring data of the sound field effect, the output parameters of the audio equipment are gradually adjusted. By analyzing the differences in the sound field coverage range and the uneven sound pressure distribution area, the output configuration is corrected to obtain the dynamic adjustment result.

6. The control system of the stage intelligent sound control audio according to claim 1, characterized in that The particle swarm optimization algorithm, according to the formula: Wherein: is the velocity value of particle j at the (k + 1)-th iteration, φ is the inertia weight, is the velocity value of particle j at the k-th iteration, a1 is the learning factor, b1 is a random number, q j is the adaptation position reached by particle j in historical iterations, is the position of particle j at the k-th iteration, a2 is the learning factor, b2 is a random number, z is the global adaptation position in the history of all particles, ρ is the sound field coverage weight factor, c j is the dynamic environment correction coefficient, is the current position of the particle is the deviation value from the target sound field coverage area, η is the sound directivity weight factor, d j is the sound output adjustment correction coefficient, is the current position of the particle is the optimized value of the sound source directivity corresponding to it.

7. The control system of the stage intelligent sound control audio according to claim 6, characterized in that, The volume fine adjustment module includes a volume fine-tuning sub-module, a sound field balance optimization sub-module, and a fine adjustment result generation sub-module, where: Volume fine-tuning sub-module: Based on the dynamic adjustment result, by adjusting the output volume intensity and frequency distribution value of the audio equipment in different regions, setting the relative power ratio between the devices and verifying the sound field coverage of the local region, generate the volume fine-tuning optimization parameters; Sound field balance optimization sub-module: Based on the volume fine-tuning optimization parameters, by measuring the sound pressure level differences in each region and optimizing the volume distribution in turn, compare and adjust the sound field strength relationship in the covered region to obtain the sound field balance optimization data; Fine adjustment result generation sub-module: Based on the sound field balance optimization data, by optimizing the output frequency, power, and coverage angle of the audio equipment in different regions, verify and correct the uniformity of the global sound field distribution, and generate the fine adjustment result.

8. The control system of the stage intelligent sound control audio according to claim 1, wherein The volume effect evaluation module includes a volume output monitoring sub-module, a listener experience evaluation sub-module, and a sound field uniformity analysis sub-module, where: Volume output monitoring sub-module: Based on the fine adjustment result, use a sound level meter to collect volume data at fixed points in different regions, and at the same time record the sound pressure level and volume fluctuation range in each region, organize the collected sound pressure data and mark the time axis to generate the volume output monitoring data; Listener experience evaluation sub-module: Based on the volume output monitoring data, by collecting feedback data from listeners in different regions and analyzing the auditory comfort in combination with the change of sound pressure level, classify and statistically analyze the feedback data to obtain the auditory perception distribution in each region, and obtain the listener experience evaluation data; Sound field uniformity analysis sub-module: Based on the listener experience evaluation data, calculate the distribution uniformity of the sound field by comparing the sound pressure levels in each region, record the regions with large sound pressure level differences, and obtain the adjustment effect evaluation by summarizing the specific values and distribution characteristics of the sound pressure differences between regions.

9. The control system of the stage intelligent sound control audio according to claim 8, characterized in that, Collect feedback data from audiences in different areas and analyze the auditory comfort by combining the sound pressure level changes. Based on the sound pressure level changes and audience feedback data in different areas, record the subjective evaluations of the audience on volume comfort, auditory fatigue, and sound clarity by setting fixed-point collection feedback within the area. Organize the audience feedback data into three dimensions: scoring data, perceptual description, and area distribution data. At the same time, combine the data trend of the sound pressure level changes, perform interval matching and classification statistics between the fluctuation range of the sound pressure level and the comfort score of the audience, analyze the influence relationship between the sound pressure level changes and the auditory comfort, and summarize the auditory perception distribution.

10. A control method for a stage intelligent sound control audio, characterized in that, Execute according to the control system of the stage intelligent sound control audio according to any one of claims 1-9, including the following steps: Step 1: Based on the real-time volume data obtained by the environmental noise sensor, segment the volume change curve for each time period, calculate the volume fluctuation amplitude, extract the peak points and valley points of the fluctuation, analyze the fluctuation frequency and amplitude changes, compare the change trend with the established standard threshold, and generate an environmental volume prediction model through multi-segment interval calculation. Step 2: Based on the environmental volume prediction model, call the venue layout information and audience distribution data, extract the audience distribution density and spatial parameters from the sound equipment within each sound field area, calculate the sound pressure coverage range within the area, compare the sound pressure coverage values of each area with the venue sound field requirements, extract the insufficient coverage parameters and deduce the adjustment range, combine the sound field reflection characteristics of the venue and the target volume distribution parameters, adjust the regional sound pressure matching value, and use the Kriging interpolation algorithm to fit and supplement the parameters of the multi-area sound field data to generate volume adjustment strategy parameters. Step 3: Based on the volume adjustment strategy parameters, dynamically adjust the output settings of the sound equipment, calculate the corresponding values of the output power and frequency band range of each device, control the output power of the devices in different partitions by matching the device power and the sound pressure coverage requirements of each partition one by one, gradually allocate the volume output parameters to each device, and use the particle swarm optimization algorithm to optimize the allocation of the device output configuration to complete the dynamic output adjustment and generate the dynamic output configuration parameters of the sound equipment. Step 4: Based on the dynamic output configuration parameters of the sound equipment, make a small-range adjustment to the sound pressure distribution value within each partition, compare the sound pressure mean value within the partition with the target sound field distribution value one by one, calculate the fine-tuning parameter range, and finally complete the compensation of the sound pressure value and the optimization of the device output by optimizing the power output and frequency divider frequency band settings of each device to generate the volume fine optimization result. Step 5: Based on the volume fine optimization result, monitor the real-time volume value output by the sound equipment through a sound level meter, extract the sound pressure distribution and uniformity parameters within each partition sound field, compare the sound pressure distribution data with the optimization result parameters, calculate the sound field uniformity deviation and the distribution of the volume change perceived by the audience, evaluate the overall volume adjustment effect, and finally generate the sound field adjustment evaluation data.

Images