Sound control method and system based on AI Internet of Things technology

Abstract

The invention discloses a sound equipment control method and system based on the AI Internet of Things technology, and relates to the technical field of sound equipment signal processing, and the method comprises the steps: S1, collecting the acoustic characteristic data of a target space in real time through an Internet of Things sensor, and S2, analyzing the acoustic characteristics of the space through an artificial intelligence model based on the collected data, s3, generating a preliminary control instruction of sound equipment equalizer parameters according to an analysis result, S4, optimizing the preliminary control instruction through a path planning model, generating an optimized regulation and control instruction, and sending the regulation and control instruction to the sound equipment to adjust the equalizer parameters in real time to solve the problem of sound distortion; according to the sound equipment control method and system based on the AI Internet of Things technology, the sound equipment equalizer parameters are regulated and controlled in real time according to the spatial acoustic characteristics so as to solve the problem of sound distortion.

Description

Technical Field

[0001] The present invention relates to the field of audio signal processing technology, and in particular to an audio control method and system based on AI Internet of Things technology. Background Art

[0002] With the rapid development of artificial intelligence and the Internet of Things (IoT), smart audio systems are becoming increasingly popular in a variety of applications, including home entertainment, in-car systems, and conference venues. Existing audio control methods primarily rely on users selecting fixed equalizer presets, such as "cinema mode" or "voice enhancement." These modes are tuned for general environments and don't account for variations in the acoustic characteristics of a given space.

[0003] In reality, room structure, furniture layout, sound-absorbing materials, and human activity can significantly influence the propagation and reflection of sound. Existing systems lack effective mechanisms to sense and respond to these spatial acoustic parameters, often resulting in sound distortion such as frequency shift, echo enhancement, or severe reverberation, which compromises the user's listening experience. To address these issues, some high-end audio systems have introduced automatic environmental awareness and acoustic compensation features, attempting to capture sound signals through built-in microphones and adaptively adjust parameters. However, these systems typically employ static models or rely on initial tuning conditions, failing to continuously track and respond to dynamic changes in the spatial environment. For example, when furniture moves, curtains open and close, or people move around, the originally adjusted equalizer parameters are difficult to maintain, resulting in unbalanced audio output. More importantly, these approaches generally lack AI-driven learning and optimization mechanisms, and are unable to accumulate optimization strategies based on historical environmental data. Summary of the Invention

[0004] The purpose of the present invention is to provide an audio control method and system based on AI Internet of Things technology, which can adjust the audio equalizer parameters in real time according to the spatial acoustic characteristics to solve the problem of sound distortion.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a sound control method based on AI Internet of Things technology, the method comprising:

[0006] S1. Collect acoustic characteristic data of the target space in real time through IoT sensors;

[0007] S2. Analyze the acoustic characteristics of the space using an artificial intelligence model based on the collected data. This includes obtaining the reverberation time value of the current point in the space in real time through sensors. Simultaneously, the artificial intelligence model outputs the optimal reverberation time value to be achieved in the current environment, determines the reverberation time deviation value, and performs attribution analysis on the reverberation time deviation value.

[0008] S3. Generating preliminary control instructions for the audio equalizer parameters based on the analysis results, including the artificial intelligence model outputting adjustment values ​​for each frequency band based on the analysis results, and the artificial intelligence model assigning weights to the frequency bands based on their assessed importance to determine the preliminary control instructions;

[0009] S4. Optimize the preliminary control instructions through the path planning model to generate optimized control instructions, and send the control instructions to the audio equipment to adjust the equalizer parameters in real time to solve the sound distortion problem. The control instructions include the actual gain value of each frequency band to achieve intelligent priority control.

[0010] Preferably, the specific step of determining the reverberation time deviation value in S2 includes subtracting the optimal reverberation time value that should be achieved under the current environment from the reverberation time value at the current point to obtain the reverberation time deviation value.

[0011] Preferably, the specific method for determining the final control instruction in S3 includes multiplying the frequency band weight by the frequency band adjustment value to obtain the weighted adjustment value of each frequency band; integrating the weighted adjustment values ​​of all frequency bands to generate a preliminary control instruction, which is an instruction set containing the weighted adjustment value of each frequency band, used to guide the parameter adjustment of the audio equalizer.

[0012] Preferably, S1 also includes using IoT sensors to monitor the acoustic characteristic data of the target space in real time, establishing a model for the propagation process of the sound wave data in the space, calculating the sound energy distribution in each area, predicting the propagation of sound energy over time, and quantifying the sound energy intensity at different spatial positions.

[0013] Preferably, the specific formula for calculating the sound energy distribution in each area is:

[0014] A(t)=A0e -kt ;

[0015] Where A(t) represents the sound energy distribution in the region, t represents time, A0 represents the initial sound pressure intensity, e represents the base of the natural logarithm, and k represents the attenuation coefficient of the sound wave during propagation in the target space.

[0016] Preferably, the specific method for adjusting the equalizer parameters in real time in S4 includes optimizing the preliminary control instructions through a path planning model, calculating the cost of adjusting each frequency band, and performing weighted summation of the adjustment costs of all frequency bands to generate a comprehensive cost function; solving the comprehensive cost function using an optimization algorithm to determine the optimal adjustment path; generating optimized control instructions based on the optimal adjustment path, the control instructions including the actual gain value of each frequency band; sending the optimized control instructions to the audio equipment through the network, and the audio equipment adjusting the equalizer parameters according to the control instructions to achieve precise sound effect control.

[0017] Preferably, the IoT sensor in S1 obtains the sound characteristic data of the target space in real time through multiple data acquisition methods, and the multiple data acquisition methods include audio sensors, environmental sensors, and temperature and humidity sensors; the audio sensor is used to collect acoustic parameters such as sound pressure, frequency, reverberation, and echo; the environmental sensor is used to collect environmental-related data such as environmental noise level and spatial layout changes; the temperature and humidity sensor is used to collect environmental parameters such as temperature and humidity; all collected data are transmitted to the artificial intelligence model in real time for comprehensive analysis to more accurately evaluate the acoustic characteristics of the target space.

[0018] Preferably, S3 also includes automatically generating personalized equalizer parameters based on the preference settings of users in different spaces, specifically: collecting users' sound preference data through mobile devices, the preference data including users' volume preferences, timbre preferences and sound requirements for different frequency bands and specific scenarios; inputting users' preference data into an artificial intelligence model, and the artificial intelligence model generates personalized equalizer parameters based on users' preference data and acoustic characteristic data of the target space.

[0019] Preferably, the IoT sensor collects and transmits data in real time through timing synchronization technology, ensuring high-precision synchronous analysis of sound characteristic data in dynamic environmental changes.

[0020] An audio control system based on AI Internet of Things technology, used to implement the steps of the audio control method based on AI Internet of Things technology, the system comprising:

[0021] IoT sensors, used to collect acoustic characteristic data of the target space in real time;

[0022] An artificial intelligence analysis model is used to analyze the spatial acoustic characteristics based on the collected data and generate preliminary control instructions for the sound equalizer parameters based on the analysis results;

[0023] Path planning model: used to optimize preliminary control instructions, calculate the cost of each frequency band adjustment, and perform weighted summation of all frequency band adjustment costs to generate a comprehensive cost function. The comprehensive cost function is then solved to determine the optimal adjustment path; optimized control instructions are generated based on the optimal adjustment path.

[0024] Control instruction transmission module: used to send the optimized control instructions to the audio equipment through the network. The audio equipment adjusts the equalizer parameters according to the control instructions to achieve precise sound effect control and solve the sound distortion problem.

[0025] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0026] This AI-based audio control method and system uses IoT sensors to collect real-time acoustic data of a target space. Based on this data, it uses an AI model to analyze the spatial acoustics. Based on the analysis results, it generates real-time control instructions for the audio equalizer parameters. These control instructions are sent to the audio equipment to adjust the equalizer parameters in real time to address sound distortion. This system can respond to environmental changes in real time, such as room structure adjustments, furniture layout changes, or human activity, ensuring optimal sound output in any environment. It intelligently optimizes the gain of each frequency band to eliminate problems such as frequency offset, echo enhancement, and excessive reverberation, effectively preventing sound distortion and enhancing the user's listening experience. It learns the user's sound preferences and the changing patterns of the spatial environment to provide personalized audio adjustment solutions. Compared to traditional audio systems that rely on static models, this system offers greater adaptability and long-term optimization capabilities, enabling precise adjustment and optimization of sound effects. Users only need to enjoy the best sound quality without worrying about detailed adjustments, simplifying the operation process. The system adjusts the audio equalizer parameters in real time based on the acoustic characteristics of the space to address sound distortion. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the process of the present invention.

[0028] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0029] 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 ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] Example 1:

[0031] like Figure 1 As shown, the present invention provides a technical solution: a sound control method based on AI Internet of Things technology, the method comprising:

[0032] S1. Collect acoustic characteristic data of the target space in real time through IoT sensors;

[0033] S2. Analyze the acoustic characteristics of the space using an artificial intelligence model based on the collected data. This includes obtaining the reverberation time value of the current point in the space in real time through sensors. Simultaneously, the artificial intelligence model outputs the optimal reverberation time value to be achieved in the current environment, determines the reverberation time deviation value, and performs attribution analysis on the reverberation time deviation value.

[0034] S3. Generating preliminary control instructions for the audio equalizer parameters based on the analysis results, including the artificial intelligence model outputting adjustment values ​​for each frequency band based on the analysis results, and the artificial intelligence model assigning weights to the frequency bands based on their assessed importance to determine the preliminary control instructions;

[0035] S4. Optimize the preliminary control instructions through the path planning model to generate optimized control instructions, and send the control instructions to the audio equipment to adjust the equalizer parameters in real time to solve the sound distortion problem. The control instructions include the actual gain value of each frequency band to achieve intelligent priority control.

[0036] This AI-based IoT-based sound control method uses IoT sensors to collect real-time acoustic data about the target space, specifically the reverberation time at the current point in the space. These sensors measure and transmit the ambient sound spectrum, noise level, echo, and other acoustic characteristics, ensuring complete acoustic information within the target space. The sensors then feed this real-time data back to the AI ​​system for further processing and analysis.

[0037] After receiving this data, the artificial intelligence model first analyzes the acoustic characteristics of the space based on a predetermined algorithm, especially calculating the reverberation time in the current environment. The reverberation time is the rate at which sound decays in space, which directly affects the clarity of the sound and the acoustic effect of the space. The artificial intelligence model compares it with the real-time data in the environment to obtain the difference between the actual reverberation time of the current space and the optimal reverberation time, that is, the reverberation time deviation value. The calculation of this deviation value not only takes into account the current environmental conditions, but also includes factors such as the size, shape, and material properties of the space.

[0038] By attributing the reverberation time deviations, the AI ​​model further identifies the specific sources of sound distortion, such as sound reflections, insufficient sound-absorbing materials, or improper spatial layout. This analysis enables the system to optimize and adjust the audio settings to improve sound quality.

[0039] Based on these analysis results, the AI ​​model generates real-time control instructions for the audio equalizer parameters. Specifically, the model outputs adjustment values ​​for each frequency band and assigns different weights to each frequency band based on its impact on the overall sound quality. Weights are assigned based on the importance of the frequency band's contribution to the final sound quality, ensuring appropriate optimization priorities. For example, low-frequency bands may be significantly affected by reflections, while high-frequency bands may suffer from sound distortion due to air absorption. Through this intelligent priority control, the system can flexibly adjust the gain of each frequency band to ensure that the sound output meets the optimal requirements of the spatial acoustics.

[0040] These optimized control commands are transmitted to the audio equipment in real time via the IoT protocol. Based on these commands, the equipment automatically adjusts the equalizer parameters, optimizes the gain of each frequency band, and effectively resolves audio distortion. The equipment not only adjusts audio output based on real-time changes in the spatial environment but also adapts itself to dynamic acoustic environments, achieving optimal sound quality. The entire process is fully controlled by an artificial intelligence system, which automatically collects, analyzes, and adjusts acoustic data, providing real-time feedback on optimization results. This ensures stable, high-quality sound in diverse spatial environments without the need for human intervention.

[0041] This AI-based audio control method, based on IoT technology, significantly improves the performance and adaptability of audio equipment through intelligent, real-time acoustic data collection and analysis. First, this method automatically analyzes the acoustic characteristics of the target space and adjusts audio equalizer parameters in real time based on the actual needs of the current environment, effectively addressing the audio distortion caused by traditional audio equipment's inability to cope with complex acoustic environments. Second, by using an AI model to attribute reverberation time deviations, it accurately identifies and optimizes acoustic issues within the space without manual intervention, reducing operational difficulty and labor costs. Furthermore, this method dynamically adjusts audio frequency bands through intelligent priority control, ensuring optimal sound quality in different spatial environments and enhancing the stability and reliability of the audio system. Furthermore, the system can flexibly adapt to changing environments and meet the needs of diverse scenarios, enhancing the device's versatility. Ultimately, this method not only improves sound quality but also reduces the complexity of adjusting audio equipment, increasing user convenience and making audio equipment more intelligent and efficient.

[0042] Example 2:

[0043] The specific step of determining the reverberation time deviation value in S2 includes subtracting the optimal reverberation time value that should be achieved in the current environment from the reverberation time value at the current point to obtain the reverberation time deviation value.

[0044] In this implementation, IoT sensors first collect real-time acoustic characteristic data of the target space, obtaining the reverberation time value at the current point in the space. Next, the AI ​​model compares this reverberation time value with the optimal reverberation time value achieved in the current environment and calculates the reverberation time deviation. Specifically, the reverberation time deviation is calculated by subtracting the optimal reverberation time value from the reverberation time value in the current environment. This deviation reflects the degree of distortion in the sound output within the target space. The AI ​​model further analyzes this deviation and attributes it to various factors in the acoustic environment, such as sound wave reflection, the distribution of sound-absorbing materials, and the spatial layout, thereby identifying the root cause of the sound quality distortion. This attribution analysis helps the AI ​​system intelligently adjust the audio equipment to reduce reverberation time deviation, improve sound quality, and optimize the acoustic effect.

[0045] Example 3:

[0046] The specific method for determining the final control instruction in S3 includes multiplying the frequency band weight by the frequency band adjustment value to obtain the weighted adjustment value of each frequency band; integrating the weighted adjustment values ​​of all frequency bands to generate a preliminary control instruction, which is an instruction set containing the weighted adjustment value of each frequency band, used to guide the parameter adjustment of the audio equalizer.

[0047] In step S3, the artificial intelligence model determines the final control instructions by analyzing the acoustic data and reverberation time deviation values ​​in the previous steps. Specifically, the artificial intelligence model first calculates according to the adjustment value of each frequency band (that is, the adjustment amount of the sound gain of each frequency band). Then, the model multiplies the adjustment value of each frequency band by the importance weight of the frequency band to obtain the weighted adjustment value of each frequency band. The determination of the frequency band weight is based on the degree of influence of the frequency band on the sound quality, usually considering the contribution of the low-frequency band and the high-frequency band to the sound quality, as well as their sensitivity in different spatial acoustic environments. Through the weighted adjustment value, the artificial intelligence model can calculate the final adjustment instruction for each frequency band. The final control instruction combines the adjustment value of each frequency band and its weight, so that the audio equipment can accurately adjust the gain value of each frequency band according to these comprehensive instructions, optimize the sound quality performance, and solve the problem of sound distortion.

[0048] By multiplying the frequency band weight by the frequency band adjustment value, the final control instruction generated can be intelligently and dynamically adjusted according to the importance of each frequency band, thereby achieving precise audio adjustment of the audio equipment in different spatial acoustic environments. This method makes the audio adjustment process more intelligent. By considering the contribution of each frequency band to the sound quality, it avoids the simple gain adjustment in the traditional method and optimizes the performance of the entire audio system. In addition, the introduction of frequency band weights enhances the adaptability of the system, and can flexibly adjust the audio output according to changes in the actual environment, ensuring the best sound quality in different usage scenarios. Users do not need to manually adjust the audio equipment. The system greatly simplifies the operation process and improves the user experience through automated intelligent adjustment. At the same time, through precise frequency band adjustment, the sound distortion problem is significantly reduced, providing clearer and more realistic sound quality, and greatly enhancing the expressiveness and reliability of the audio equipment.

[0049] Example 4:

[0050] S1 involves using IoT sensors to monitor the acoustic characteristics of the target space in real time, modeling the propagation of sound waves in space, calculating the distribution of sound energy in each area, predicting the propagation of sound energy over time, and quantifying the intensity of sound energy at different spatial locations.

[0051] In step S1, IoT sensors capture information about sound wave propagation within the target space by monitoring its acoustic characteristics in real time. Specifically, IoT sensors not only collect acoustic data within the space but also model the propagation of sound waves within it. This model analyzes the propagation of sound waves from the source to different spatial locations and calculates the distribution of sound energy in each area. Based on the propagation patterns of sound waves, the system can predict how sound energy changes over time and quantify the intensity of sound energy at different spatial locations. This process provides accurate spatial acoustic information for further analysis and optimization of audio equipment settings, making subsequent reverberation time analysis and the generation of equalizer adjustment instructions more scientific and precise.

[0052] By using IoT sensors to monitor the propagation of sound waves in real time and establishing a spatial acoustic energy distribution model, the system can gain a deep understanding of the propagation characteristics and energy distribution of sound waves within a space. This method accurately quantifies the acoustic energy intensity at different spatial locations, providing a comprehensive understanding of the spatial acoustic environment and ensuring that audio equipment can be optimized based on the specific acoustic characteristics of the target space. This modeling and prediction of the sound wave propagation process improves the adaptability of audio equipment to complex environments, enabling more efficient acoustic adjustments and ultimately delivering optimal sound quality. Furthermore, the ability to collect and predict real-time acoustic data enables continuous optimization in dynamic environments, further enhancing the intelligence and automation of the audio system. This not only reduces the operational burden on users but also enhances the intelligent responsiveness and operational precision of audio equipment, significantly improving the user experience.

[0053] The specific formula for calculating the sound energy distribution in each area in S1 is:

[0054] A(t)=A0e -kt ;

[0055] Where A(t) represents the sound energy distribution in the region, t represents time, A0 represents the initial sound pressure intensity, e represents the base of the natural logarithm, and k represents the attenuation coefficient of the sound wave during propagation in the target space.

[0056] IoT sensors monitor the acoustic characteristics of the target space and calculate and analyze the acoustic energy distribution in each area. To accurately quantify the change in acoustic energy over time, the system uses a sound wave attenuation model to describe the acoustic energy propagation process. Specifically, the change in acoustic energy distribution is expressed by the following formula:

[0057] A(t)=A0e -kt ;

[0058] Where A(t) represents the sound energy distribution in the region, t represents time, A0 represents the initial sound pressure intensity, e represents the base of the natural logarithm, and k represents the attenuation coefficient of the sound wave during propagation in the target space.

[0059] This formula accurately simulates the propagation and attenuation of sound waves in space, providing precise acoustic data for adjusting audio equipment. This model calculates the corresponding sound energy intensity in each area of ​​the space and predicts its temporal evolution. This enables the audio system to monitor changes in the acoustic environment in real time and adjust the audio equipment based on the predicted sound energy distribution, ensuring optimal sound quality in different areas.

[0060] By using a sound wave attenuation model to calculate and predict the distribution of sound energy, this method can accurately quantify the sound energy intensity in different areas of the target space and track the changes in sound energy over time in real time. This model provides more accurate acoustic data support, which can help audio equipment intelligently adjust its output parameters and optimize sound quality performance. By considering the attenuation effect of sound wave propagation, the system can more scientifically predict the acoustic environment of each area in the space, ensuring that the audio equipment can provide ideal sound quality effects in different environments. In addition, this method enhances the system's dynamic adaptability and can automatically adjust the settings of the audio equipment when the spatial acoustic environment changes, thereby improving the intelligence level of the audio system and the user experience.

[0061] A(t) represents the acoustic energy distribution in the target area at time t, indicating the energy intensity of the sound wave at different locations in the target space, which decays over time. A0 represents the initial sound pressure, representing the sound wave intensity at the initial time t = 0. It is typically determined by measuring the output of the audio equipment or the sound pressure intensity at the sound source. t represents the time elapsed from the emission of the sound wave from the sound source to the current moment. This is typically calculated using the system's internal time synchronization mechanism. The attenuation coefficient k reflects the attenuation rate of the sound wave as it propagates through space. It is primarily influenced by factors such as the size, shape, and sound-absorbing materials of the space and is typically determined through experimental measurements or acoustic modeling. Experimental data can be collected using IoT sensors to collect acoustic energy values ​​at different locations. Based on the propagation characteristics of the sound wave, the value of k is inferred using optimization algorithms or theoretical models. In summary, by properly determining these parameters, the system can accurately simulate the acoustic energy decay process within a space, providing scientific data support for adjusting audio equipment, optimizing sound quality, and enhancing the intelligence of the audio system.

[0062] Example 5:

[0063] The specific method for adjusting the equalizer parameters in real time in S4 includes optimizing the preliminary control instructions through a path planning model, calculating the cost of adjusting each frequency band, and performing weighted summation of the adjustment costs of all frequency bands to generate a comprehensive cost function; solving the comprehensive cost function using an optimization algorithm to determine the optimal adjustment path; generating optimized control instructions based on the optimal adjustment path, the control instructions including the actual gain value of each frequency band; sending the optimized control instructions to the audio equipment through the network, and the audio equipment adjusting the equalizer parameters according to the control instructions to achieve precise sound effect control.

[0064] In step S4, the system uses the path planning model to calculate the adjustment amount of each frequency band and the corresponding frequency band weight to obtain the cost of adjusting each frequency band. Specifically, the frequency band adjustment cost measures the optimization cost required to adjust a certain frequency band, which is closely related to the sound effect impact and adjustment difficulty of the frequency band. For example, for low-frequency bands, their adjustment may involve more complex spatial acoustic characteristics, so there will be a higher adjustment cost; while the adjustment of high-frequency bands may be more direct and the cost is lower. The adjustment cost of each frequency band is obtained by multiplying the adjustment amount of the frequency band by the weight. This process can quantify the optimization resources required for adjusting different frequency bands.

[0065] The system then performs a weighted summation of the adjustment costs for all frequency bands. This weighted summation not only considers the adjustment costs of each frequency band but also takes into account the frequency band's importance to the overall sound quality, ensuring that the system prioritizes adjustments to frequency bands that have the greatest impact on sound quality. This weighted summation approach helps the system find the optimal adjustment path within limited resources or optimization time, avoiding over-adjustment of certain frequency bands or neglecting the adjustment of important frequency bands, thereby achieving global sound optimization.

[0066] Ultimately, the optimized adjustment path is generated as the optimal control command and sent to the audio equipment via the network. Upon receiving the optimized adjustment command, the audio equipment adjusts the equalizer parameters in real time, precisely controlling the gain value for each frequency band. This adjustment adapts to the acoustic characteristics of the space, ensuring optimal sound quality in different environments and usage scenarios. Through this intelligent adjustment process, the audio system automatically optimizes sound output without manual intervention, ensuring clear and accurate sound and avoiding distortion or imbalanced sound quality issues.

[0067] By applying the path planning model, the system can intelligently find the optimal adjustment path in the adjustment of all frequency bands, thereby optimizing the sound effect. The product calculation of the adjustment cost of each frequency band and its weight enables the system to comprehensively consider the importance and adjustment difficulty of each frequency band, and achieve more precise sound effect control. Through the weighted summation method, the system can achieve a reasonable balance between different frequency bands, avoid excessive adjustment or imbalance of certain frequency bands, and ensure the stability and balance of sound quality. In addition, this method can automatically adjust the equalizer parameters of the audio equipment according to the acoustic characteristics of the space without manual intervention, greatly improving the intelligence level and operational convenience of the audio equipment. Ultimately, users can enjoy more accurate and high-quality sound effects, while also reducing the complexity of equipment maintenance and debugging.

[0068] Example 6:

[0069] The IoT sensor in S1 obtains the sound characteristic data of the target space in real time through multiple data collection methods, including audio sensors, environmental sensors, and temperature and humidity sensors; the audio sensor is used to collect acoustic parameters such as sound pressure, frequency, reverberation, and echo; the environmental sensor is used to collect environmental-related data such as ambient noise level and spatial layout changes; the temperature and humidity sensor is used to collect environmental parameters such as temperature and humidity; all collected data is transmitted to the artificial intelligence model in real time for comprehensive analysis to more accurately evaluate the acoustic characteristics of the target space.

[0070] In step S1, IoT sensors acquire real-time acoustic data from the target space through various data collection methods. These methods include audio sensors, environmental sensors, and temperature and humidity sensors. Each sensor performs the following functions: Audio sensors collect acoustic characteristics such as sound pressure and frequency in the space, accurately capturing changes in the audio signal; environmental sensors monitor echoes and reverberations in the space, capturing reflected sound waves, which is crucial for understanding the acoustic effects of the space; and temperature and humidity sensors acquire real-time temperature and humidity data within the space, factors that directly impact the speed of sound wave propagation and sound quality.

[0071] All of these sensors capture sound characteristic data, including sound pressure, frequency, reverberation, and echo, which is then transmitted to the AI ​​model in real time. The AI ​​model analyzes this data and, by comparing actual measurements with ideal target values, automatically determines whether the acoustic characteristics of the space meet optimal sound quality requirements. If any characteristics fall short of ideal values, the model generates appropriate adjustment instructions to guide subsequent equalizer adjustments and sound optimization.

[0072] Through the combined application of multiple sensors, this method can comprehensively and accurately collect sound characteristic data in the target space. The coordinated use of audio sensors, environmental sensors, and temperature and humidity sensors enables the system to not only monitor the sound wave characteristics in the space, but also to perceive in real time the impact of environmental factors such as temperature and humidity on sound wave propagation and sound effects. This multi-dimensional data collection method greatly improves the adaptability and accuracy of the system. The real-time analysis of these multi-source data by the artificial intelligence model enables the audio equipment to be intelligently adjusted based on actual environmental conditions, optimize the sound quality performance, and avoid sound quality instability or distortion caused by environmental changes. Through this intelligent audio control, users can experience consistent high-quality sound effects without manual intervention, which improves the intelligence level of the audio equipment and the user experience.

[0073] Example 7:

[0074] S3 also includes automatically generating personalized equalizer parameters based on the preference settings of users in different spaces. Specifically, it collects users' sound preference data through mobile devices, and the preference data includes users' volume preferences, timbre preferences, and sound requirements for specific scenarios in different frequency bands; inputs the user's preference data into the artificial intelligence model, and the artificial intelligence model generates personalized equalizer parameters based on the user's preference data and the acoustic characteristic data of the target space.

[0075] In step S3, the system not only generates audio equalizer parameters based on the acoustic characteristics of the space, but also automatically generates personalized equalizer parameters based on the preferences of users in different spaces. Specifically, the system collects and analyzes the user's sound preference data, such as the user's preferences for the volume, bass, treble and other frequency bands of the audio system, and automatically adjusts the equalizer parameters in combination with the acoustic data in the space to meet personalized sound needs. To achieve this goal, the artificial intelligence model will learn and adjust based on the user's historical preferences and real-time feedback, so that the setting of the equalizer parameters is more in line with the user's expectations. For example, if the user prefers a stronger bass effect, the system will give priority to adjusting the gain value of the low frequency band; if the user likes clear high frequencies, the system will adjust the gain value of the high frequency band accordingly. Through this personalized setting, the audio system can provide the best sound experience that meets the needs of different users.

[0076] By automatically generating personalized equalizer parameters based on the sound preferences of users in different spaces, this method greatly improves the flexibility of the audio system and the user experience. Personalized sound adjustment can accurately meet the different needs of users. Whether it is a user who prefers bass effects or a user who pursues clear high frequencies, the system can perform intelligent sound optimization. The artificial intelligence model learns the user's historical preferences and real-time feedback, so that each adjustment is closer to the user's needs, avoiding the complexity of manual operation. This function not only improves the intelligence level of the audio system, but also reduces the user's operating burden through automated adjustment, allowing audio equipment to provide a more personalized and comfortable listening experience. In addition, the generation of personalized equalizer parameters ensures that users can always enjoy ideal sound performance in different spaces and usage scenarios.

[0077] Example 8:

[0078] IoT sensors collect and transmit data in real time through timing synchronization technology, ensuring high-precision synchronous analysis of sound characteristic data in dynamic environmental changes.

[0079] In step S1, IoT sensors collect and transmit data in real time using timing synchronization technology, ensuring high-precision, synchronized analysis of sound characteristic data in a dynamic environment. Specifically, timing synchronization ensures that when multiple sensors within the target space collect data simultaneously, the timestamps of each sensor remain consistent, preventing data deviations caused by time asynchrony between sensors. Through this timing synchronization, the system can accurately synchronize sound characteristic data collected by different sensors, such as sound pressure, frequency, reverberation, and echo, ensuring that these data reflect the overall acoustic environment of the space at the same time. The real-time synchronized data is processed by an artificial intelligence model to provide accurate and reliable acoustic analysis data for subsequent sound optimization. This allows the system to respond quickly to dynamically changing environmental factors within the space (such as changes in temperature and humidity, spatial layout, and external noise interference), ensuring efficient and accurate sound adjustment and avoiding sound distortion or inaccurate adjustment caused by delayed or inconsistent data.

[0080] Through timing synchronization technology, the system can achieve high-precision data synchronization between multiple sensors, ensuring the accuracy and timeliness of sound characteristic data. Especially in the case of dynamic environmental changes (such as temperature changes, humidity changes or interference from other external noise sources in the space), timing synchronization technology can ensure that the data of each sensor reflects the acoustic characteristics of the space at the same time point, avoiding data lag or misalignment, thereby improving the system's response speed and accuracy to environmental changes. This technology enables artificial intelligence models to perform more accurate acoustic analysis and sound optimization based on high-precision synchronized data, ensuring that audio equipment can always provide high-quality sound effects in different environments and time periods. Through this precise data synchronization and real-time analysis, the audio system can quickly adjust the equalizer parameters and optimize the sound quality output, greatly improving the intelligence level of audio equipment and user experience.

[0081] like Figure 2 As shown, a sound control system based on AI Internet of Things technology is also provided, which is used to implement the steps of the sound control method based on AI Internet of Things technology. The system includes:

[0082] IoT sensors, used to collect acoustic characteristic data of the target space in real time;

[0083] An artificial intelligence analysis model is used to analyze the spatial acoustic characteristics based on the collected data and generate preliminary control instructions for the sound equalizer parameters based on the analysis results;

[0084] Path planning model: used to optimize preliminary control instructions, calculate the cost of each frequency band adjustment, and perform weighted summation of all frequency band adjustment costs to generate a comprehensive cost function. The comprehensive cost function is then solved to determine the optimal adjustment path; optimized control instructions are generated based on the optimal adjustment path.

[0085] Control instruction transmission module: used to send the optimized control instructions to the audio equipment through the network. The audio equipment adjusts the equalizer parameters according to the control instructions to achieve precise sound effect control and solve the sound distortion problem.

[0086] This AI-based audio control system consists of four main modules: IoT sensors, an AI analysis module, an audio equalizer control module, and a control command transmission module. The IoT sensors collect real-time acoustic data from the target space, including sound pressure, frequency, reverberation, and echo. Timing synchronization technology ensures precise synchronization of data from multiple sensors. This collected data is transmitted to the AI ​​analysis module, which uses machine learning and data analysis techniques to analyze the acoustic characteristics of the space in real time and identify acoustic issues such as reverberation time deviation and frequency band imbalance. The audio equalizer control module then generates control commands for the audio equalizer parameters based on the AI ​​analysis results, adjusting the frequency band gain of the audio equipment in real time to optimize sound quality and address distortion. Finally, the control command transmission module transmits these optimized control commands to the audio equipment, ensuring that the equipment can adjust the equalizer parameters based on real-time analysis. This provides precise sound control in a dynamic environment and ensures optimal sound quality.

[0087] The sound control system of the present invention achieves precise control of audio equipment by effectively combining the Internet of Things sensor, artificial intelligence analysis module, audio equalizer control module and control instruction transmission module. The system can collect and analyze the acoustic characteristic data in the space in real time, automatically generate personalized sound optimization solutions, and ensure that the audio equipment can provide high-quality sound quality output under various environmental conditions. By adjusting the audio equalizer parameters in real time, the problem of sound distortion is solved and the performance and adaptability of the audio system are improved. The system has a high level of intelligence and can automatically adjust according to environmental changes and user preferences, which greatly improves the user experience and simplifies the operation process. At the same time, through high-precision data synchronization and rapid response, the system can always provide stable sound performance in dynamic environmental changes, thereby enhancing the reliability and efficiency of the audio equipment.

[0088] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A sound control method based on AI Internet of Things technology, characterized in that: The method comprises: S1. Collect acoustic characteristic data of the target space in real time through IoT sensors; S2. Analyze the acoustic characteristics of the space using an artificial intelligence model based on the collected data. This includes obtaining the reverberation time value of the current point in the space in real time through sensors. Simultaneously, the artificial intelligence model outputs the optimal reverberation time value to be achieved in the current environment, determines the reverberation time deviation value, and performs attribution analysis on the reverberation time deviation value. S3. Generating preliminary control instructions for the audio equalizer parameters based on the analysis results, including the artificial intelligence model outputting adjustment values ​​for each frequency band based on the analysis results, and the artificial intelligence model assigning weights to the frequency bands based on their assessed importance to determine the preliminary control instructions; S4. Optimize the preliminary control instructions through the path planning model to generate optimized control instructions, and send the control instructions to the audio equipment to adjust the equalizer parameters in real time to solve the sound distortion problem. The control instructions include the actual gain value of each frequency band to achieve intelligent priority control.

2. The sound control method based on AI Internet of Things technology according to claim 1, characterized in that: The specific step of determining the reverberation time deviation value in S2 includes subtracting the optimal reverberation time value that should be achieved in the current environment from the reverberation time value at the current point to obtain the reverberation time deviation value.

3. The sound control method based on AI Internet of Things technology according to claim 1, characterized in that: The specific method for determining the final control instruction in S3 includes multiplying the frequency band weight by the frequency band adjustment value to obtain the weighted adjustment value of each frequency band; integrating the weighted adjustment values ​​of all frequency bands to generate a preliminary control instruction, which is an instruction set containing the weighted adjustment values ​​of each frequency band, used to guide the parameter adjustment of the audio equalizer.

4. The sound control method based on AI Internet of Things technology according to claim 1, characterized in that: Said S1 also includes using IoT sensors to monitor the acoustic characteristic data of the target space in real time, establishing a model for the propagation process of the sound wave data in the space, calculating the sound energy distribution in each area, predicting the propagation of sound energy over time, and quantifying the sound energy intensity at different spatial positions.

5. The sound control method based on AI Internet of Things technology according to claim 4, characterized in that: The specific formula for calculating the sound energy distribution in each area is: A(t)=A0e -kt ; Where A(t) represents the sound energy distribution in the current area, t represents time, A0 represents the initial sound pressure intensity, e represents the base of the natural logarithm, and k represents the attenuation coefficient of the sound wave during propagation in the target space.

6. The sound control method based on AI Internet of Things technology according to claim 1, characterized in that: The specific method for adjusting the equalizer parameters in real time in S4 includes optimizing the preliminary control instructions through a path planning model, calculating the cost of adjusting each frequency band, and performing weighted summation of the adjustment costs of all frequency bands to generate a comprehensive cost function; solving the comprehensive cost function using an optimization algorithm to determine an optimal adjustment path; and generating an optimized control instruction based on the optimal adjustment path, wherein the control instruction includes an actual gain value for each frequency band; The optimized control instructions are sent to the audio equipment via the network, and the audio equipment adjusts the equalizer parameters according to the control instructions to achieve precise sound effect control.

7. The sound control method based on AI Internet of Things technology according to claim 1, characterized in that: The IoT sensor in S1 obtains the sound characteristic data of the target space in real time through multiple data collection methods, and the multiple data collection methods include audio sensors, environmental sensors, and temperature and humidity sensors; the audio sensor is used to collect acoustic parameters such as sound pressure, frequency, reverberation, and echo; the environmental sensor is used to collect environmental-related data such as ambient noise level and spatial layout changes; the temperature and humidity sensor is used to collect environmental parameters such as temperature and humidity; all collected data are transmitted to the artificial intelligence model in real time for comprehensive analysis to more accurately evaluate the acoustic characteristics of the target space.

8. The sound control method based on AI Internet of Things technology according to claim 1, characterized in that: The S3 also includes automatically generating personalized equalizer parameters based on the preference settings of users in different spaces, specifically: collecting users' sound preference data through mobile devices, the preference data including users' volume preferences, timbre preferences and sound requirements for different frequency bands and specific scenarios; inputting users' preference data into an artificial intelligence model, and the artificial intelligence model generates personalized equalizer parameters based on users' preference data and the acoustic characteristic data of the target space.

9. The sound control method based on AI Internet of Things technology according to claim 1, characterized in that: The IoT sensor collects and transmits data in real time through timing synchronization technology, ensuring high-precision synchronous analysis of sound characteristic data in dynamic environmental changes.

10. An audio control system based on AI Internet of Things technology, used to implement the steps of the audio control method based on AI Internet of Things technology according to any one of claims 1 to 9, characterized in that: The system comprises: IoT sensors, used to collect acoustic characteristic data of the target space in real time; An artificial intelligence analysis model is used to analyze the spatial acoustic characteristics based on the collected data and generate preliminary control instructions for the sound equalizer parameters based on the analysis results; Path planning model: used to optimize preliminary control instructions, calculate the cost of each frequency band adjustment, and perform weighted summation of all frequency band adjustment costs to generate a comprehensive cost function. The comprehensive cost function is then solved to determine the optimal adjustment path; optimized control instructions are generated based on the optimal adjustment path. Control instruction transmission module: used to send the optimized control instructions to the audio equipment through the network. The audio equipment adjusts the equalizer parameters according to the control instructions to achieve precise sound effect control and solve the sound distortion problem.

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