Intelligent rhythm light control system

Abstract

The invention requests to protect an intelligent rhythm light control system, and the system comprises an intelligent light controller which is used for downloading a sound feature driving file from a cloud end, and generating a control instruction according to the file and a material fragment file made by a light illuminator; the sound feature driving file is a corresponding sound feature driving file generated and output by adopting an AI model according to input audio data, and the sound feature driving file is stored in the cloud; the embedded mainboard is carried on the intelligent light controller and is used for executing software of music feature recognition and light arrangement; the sound card module is connected with the intelligent light controller and is used for sampling an audio signal in real time and transmitting the audio signal to the embedded mainboard; the system comprises an intelligent lighting controller, a stage lamp, an ART-NET DMX512 module, the ART-NET DMX512 module transmits a DMX512 control signal to the stage lamp based on an Art-Net / sACN protocol, and the ART-NET DMX512 module is connected with the intelligent lighting controller.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent light control, specifically to an intelligent rhythmic light control system. BACKGROUND

[0002] In the modern stage performance and entertainment industry, light effects, as an important part of audio-visual art, directly affect the audience's immersive experience due to their synchronization with music. Traditional light control methods, whether manual programming or automatic playback based on fixed templates, have certain limitations and challenges.

[0003] I. Accuracy problem Traditional light control systems often rely on pre-set timelines, but such static timelines are difficult to accurately capture the dynamic changes in music, especially complex audio features such as slight delays in drum beats or sudden vocal explosions. This lack of accuracy results in a clear time misalignment between music and light, affecting the consistency of audio-visual and emotional transmission.

[0004] II. Lack of flexibility In existing technology, light effects programming usually requires light designers to write individually for each song, which is time-consuming and difficult to adapt to frequent changes in music during performances. In addition, for different versions of the same song, traditional control systems are difficult to make corresponding adjustments, limiting the diversity and artistic creativity of light performance.

[0005] III. Limited dynamic diversity In traditional light programming, light effects are often relatively single and difficult to automatically change according to different sections and emotions of music. For example, achieving smooth transitions in color and brightness between choruses and interludes, or designing unique light responses for specific musical events such as vocal peaks and instrumental solos, are all demands that existing technology cannot meet.

[0006] IV. High production efficiency demand With the expansion of performance scale and continuous technological progress, the market's demand for quickly producing high-quality, personalized light shows is growing. However, traditional methods not only require a lot of preliminary preparation work, but also are difficult to quickly adjust in actual operation to respond to unexpected situations, reducing production efficiency and creative flexibility.

[0007] The above describes the challenges currently faced in the field of intelligent music-synchronized light control, including difficulty in accurate synchronization, lack of flexibility, limited dynamic diversity, unmet high production efficiency demand, and obstacles to compatibility and integration. These technical problems restrict the quality of performance effects and the artistic nature of stage performances, and innovative solutions are needed to break through the limitations of existing technology. SUMMARY

[0008] The present application aims to solve the above problems of the prior art. An intelligent rhythm lighting control system is proposed. The technical solution of the present application is as follows: An intelligent rhythm lighting control system, comprising: an intelligent lighting controller, configured to download a sound feature driving file from the cloud and generate control instructions according to the file and a material segment file made by a lighting designer; the sound feature driving file is generated by an AI model based on input audio data, and is stored in the cloud; an embedded mainboard, mounted on the intelligent lighting controller, configured to execute software for music feature recognition and lighting programming; a sound card module, connected with the intelligent lighting controller, configured to sample audio signals in real time and transmit the audio signals to the embedded mainboard; an ART-NET DMX512 module, configured to transmit DMX512 control signals to stage lamps based on Art-Net / sACN protocol, and the ART-NET DMX512 module is connected with the intelligent lighting controller.

[0009] Further, the sound card module includes a real-time audio processing unit to detect the kick drum / military drum hitting time point in the audio with an accuracy of ±10ms, and the detection result is used for deep feature analysis.

[0010] Further, the embedded mainboard carries a deep learning model to automatically generate lighting programming strategies adapted to the music style, including color gradient, frequency flash and intensity adjustment.

[0011] Further, the sound card module supports multi-channel input and processes multiple audio source signals in real time to realize the coordination of multi-audio source lighting effects.

[0012] Further, the intelligent lighting controller is provided with a lighting material library management system to real-time search and cache lighting materials conforming to the music features.

[0013] Further, the intelligent lighting controller uses a precision time protocol (PTP) to ensure consistent synchronization of audio-visual elements.

[0014] Further, the ART-NET DMX512 module is compatible with the Art-Net protocol.

[0015] Further, the embedded mainboard uses an emotion analysis algorithm to convert music emotions into lighting color and brightness changes in real time; the emotion analysis algorithm uses a bimodal emotion analysis algorithm combining sound and text, after obtaining the emotion labels of text and sound, uses multi-modal fusion technology, and joint encoding of neural networks to integrate the information of the two modalities; during the fusion process, the weights of the two are dynamically adjusted to obtain the optimal emotion judgment.

[0016] Further, the real-time audio processing unit employs a short-time Fourier transform algorithm to accurately detect specific drum beat frequencies. The advantages and beneficial effects of the present application are as follows:

[0017] The present application realizes perfect synchronization of music and lighting by integrating intelligent audio analysis and high-precision lighting control technology. The core advantage of the system lies in its high intelligence and precise response capability, which can analyze the rhythm and emotion of music in real time and instantly generate matching lighting effects, significantly improving the consistency and immersion of audio-visual experience. The multi-channel input function enables precise processing of complex stage environments with multiple sound sources, while the adoption of precise time protocol further ensures seamless integration of lighting and sound, providing an unparalleled audio-visual feast for the audience. In addition, the system supports multiple mainstream control protocols, has wide compatibility, and is easy to deploy and apply in different devices and scenes, greatly expanding the use range of the system and providing strong technical support for stage art innovation.

[0018] 1. Seven-dimensional music feature extraction engine Innovative point: The present application first proposes and realizes a comprehensive music feature recognition architecture, including beat recognition, style classification, drum beat detection, music segmentation, event capture, color analysis, and lighting driving file generation. This multi-dimensional music analysis technology can accurately capture the subtle changes of music and provide rich input data for lighting control.

[0019] 2. Dynamic programming mechanism and randomization engine Innovative point: The dynamic programming mechanism of the present application can adjust the lighting effect in real time according to the music features, combined with the randomization engine, to generate unique lighting solutions for each performance. This mechanism not only adapts to changing music styles, but also creates unexpected visual effects, enhancing the watchability and artisticity of the performance.

[0020] 3. Precise time code synchronization technology Innovative point: Through the precise time protocol (PTP), sub-millisecond time synchronization is achieved, ensuring millisecond-level precise alignment between music features and lighting effects. This high-precision synchronization technology is one of the core advantages of the system.

[0021] 4. Intelligent management and application of lighting material library Innovative point: The present application establishes a modular storage lighting material library, in which the materials are intelligently labeled and can be automatically matched and optimized according to music features. This intelligent material library management mechanism greatly improves the efficiency and diversity of lighting design.

[0022] 5. Efficient music-light mapping algorithm Innovative point: The application realizes efficient mapping from music features to light effects through deep learning and intelligent algorithms, and can quickly generate light schemes that adapt to different music styles.

[0023] 6. Creativity of emotion analysis algorithm: Traditional single-modal emotion analysis algorithms, whether based on text or sound, can only capture one side of emotional expression and cannot fully understand the complexity of emotions. This algorithm creatively combines text emotion analysis and sound emotion analysis in two modalities to comprehensively judge the emotional state from multiple angles, which is the core innovation. By considering both the content of language and the emotional color of sound, it can more accurately identify the true feelings of the speaker, even the subtle emotional fluctuations hidden behind the language are not easily overlooked. In the process of multi-modal fusion, the algorithm automatically learns and dynamically adjusts the weights of text and sound modalities, which is a breakthrough in emotion analysis. This is because in different situations, people's emotional expression may focus on different aspects, sometimes the content of the language better reflects the true emotions, and sometimes the tone or background music plays a leading role. By dynamically adjusting the weights, the algorithm can more flexibly allocate attention according to different situations, so as to make more objective and comprehensive emotional judgments. The introduction of cross-modal consistency loss function is another important innovation. This mechanism ensures that when analyzing text and sound simultaneously, the emotional labels of the two modalities are logically consistent, even when dealing with noisy data, it can maintain a high accuracy. This method effectively solves the problem of information mismatch between modalities, ensuring that the algorithm does not deviate from the final result due to errors in a single modality when fusing multi-source information. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a schematic diagram of the structure of the intelligent rhythm light control system provided by the application. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the application will be described in detail below with reference to the accompanying drawings. The described embodiments are only a part of the embodiments of the application.

[0026] The technical solution of the application to solve the above technical problems is: As Figure 1As shown, the present invention provides an intelligent music-synchronized lighting control system, including an intelligent lighting controller, which is used to download a sound feature drive file from the cloud and generate control instructions based on the file and a material clip file produced by a lighting engineer; the sound feature drive file is generated by an AI model based on the input audio data and outputs a corresponding sound feature drive file, which is stored in the cloud; an embedded mainboard, which is mounted on the intelligent lighting controller and is used to execute software for music feature recognition and lighting arrangement; a sound card module, which is connected to the intelligent lighting controller and is used to sample audio signals in real time and transmit the audio signals to the embedded mainboard; an ART-NET DMX512 module, which transmits DMX512 control signals to stage lamps based on the Art-Net protocol, and the ART-NET DMX512 module is connected to the intelligent lighting controller.

[0027] Preferably, when the device is customized for a KTV, it also includes a relay control board, which is connected to the intelligent lighting controller and is used to receive control commands and turn the 220V lighting equipment on and off; a switch module, which is connected to the intelligent lighting controller and is used to facilitate data exchange within the system; and a single-chip microcomputer motherboard, which is connected to the relay control board and external devices and is used to receive control commands from the intelligent lighting controller and control the operation of the relay control board and external devices. Technically, these components are tightly integrated to form a closed-loop control system, ensuring a seamless transition from audio signal acquisition to lighting effect presentation. In principle, the sound card module captures audio signals, the embedded motherboard analyzes signal characteristics and generates control commands, the single-chip motherboard executes the commands, and the ART-NET DMX512 module is responsible for converting the commands into DMX512 signals that the lighting fixtures can understand. Effectively, the technology in this embodiment achieves precise synchronization between music and lighting, enhancing the audiovisual experience of the performance. In other embodiments, additional sensors (such as vibration sensors) can be added to further refine the perception of musical rhythm and address the issue of inaccurate audio signal acquisition in certain environments.

[0028] Further, the sound card module further includes a real-time audio processing unit for detecting the kick drum hitting time points in the audio, and the detection results are transmitted to the embedded mainboard for deep feature analysis, wherein the detection accuracy reaches ±10 ms. In terms of technology, the real-time audio processing unit adopts advanced signal processing algorithms to ensure accurate capture of drum points. In principle, short-time Fourier transform (STFT) and machine learning models are used to improve the accuracy of kick drum detection. In terms of effect, the technology in this embodiment enables the lights to respond instantly to key drum points in the music, enhancing the dynamic atmosphere of the live performance. In other embodiments, more advanced audio analysis techniques such as deep neural networks can be integrated to improve the robustness and accuracy of drum point detection, solving the drum point recognition problem in complex music environments.

[0029] Further, the embedded mainboard further carries a deep learning model for analyzing music sentiment and rhythm changes, automatically generating light programming strategies that adapt to different music styles, including color gradient, stroboscopic mode and light intensity adjustment. In terms of technology, the deep learning model is trained to recognize various music features such as rhythm, melody and emotion. In principle, the model predicts suitable light effects by analyzing audio spectrum and timing information. In terms of effect, the technology in this embodiment can automatically adjust the lights according to different parts of the music, creating a more rich and music-matching visual effect. In other embodiments, more music features such as instrument type and voice analysis can be introduced to further optimize light programming and solve the problem of matching lights for specific music types.

[0030] Further, the sound card module has multi-channel input function, which can simultaneously receive and process audio signals of multiple sound sources, and realize seamless switching and coordination of light effects among multiple sound sources through the intelligent light controller. In terms of technology, the multi-channel input function allows the system to handle complex audio scenarios such as live band and background music mixing. In principle, the system can intelligently analyze the characteristics of each channel to ensure that the light effects are harmonious and unified with all sound sources. In terms of effect, the technology in this embodiment enables the system to flexibly cope with variable audio inputs in large-scale performances, providing consistent audio-visual enjoyment. In other embodiments, the number of channels can be increased and signal separation algorithms can be optimized to improve the efficiency and quality of multi-source processing, solving the performance bottleneck in high-density audio input environments.

[0031] Further, the intelligent light controller further includes a light material library management system that can retrieve, load, and cache corresponding light materials in real time according to music characteristics. In terms of technology, the light material library management system adopts an efficient indexing and caching mechanism. In terms of principle, the system quickly locates appropriate light effects through music characteristic tags, reducing material loading time. In terms of effect, the technology in this embodiment improves the response speed and diversity of light effects, providing audiences with a more stunning visual impact. In other embodiments, the selection range of light materials can be expanded by introducing a cloud material library and using remote data access technology to solve the problem of local storage space limitations.

[0032] Further, the intelligent light controller further adopts the Precision Time Protocol (PTP) to achieve time code alignment with stage sound and other control systems, ensuring the consistency and synchronization of all audio-visual elements. In terms of technology, the PTP protocol provides high-precision time synchronization capabilities. In terms of principle, network clock synchronization technology is used to ensure time consistency between internal and external devices of the system. In terms of effect, the technology in this embodiment eliminates delays between audio-visual elements and improves the overall coordination of the performance. In other embodiments, network architecture can be optimized and redundant time synchronization paths can be added to improve system stability and reliability, solving the problem of time synchronization in the event of network failure.

[0033] Further, the ART-NET DMX512 module further supports the Art-Net network control protocol, enabling seamless integration with mainstream stage light control systems on the market. In terms of technology, the ART-NET DMX512 module has strong protocol compatibility. In terms of principle, a built-in protocol converter is used to enable communication between different control systems. In terms of effect, the technology in this embodiment simplifies integration with other light control systems, improving the system's versatility and interoperability. In other embodiments, support for more protocols, such as DMX over IP, can be added to expand the system's application range and solve the problem of insufficient protocol compatibility in specific environments.

[0034] Further, the embedded motherboard further uses emotion analysis algorithms. Traditional emotion analysis algorithms are usually limited to a single modality of text or speech, but human emotions are often multi-dimensional, including language content, tone, speech rate, and non-verbal background music. To more accurately capture and understand complex emotions, we propose combining text-based emotion analysis algorithms with sound-based emotion analysis algorithms to form a multi-modal emotion analysis framework.

[0035] We apply deep learning models to the analysis of text and sound signals, and by fusing the results of both, we obtain a more comprehensive and accurate emotion judgment. Detailed Steps

[0036] 1. Text Sentiment Analysis Algorithm - Use pre-trained natural language processing (NLP) models such as BERT, RoBERTa, or XLNet to perform sentiment analysis on the text.

[0037] - Process text data such as social media posts, chat logs, song lyrics, etc., to identify positive, negative, and neutral sentiment tendencies, as well as more nuanced emotional states such as anger, joy, sadness, surprise, etc.

[0038] - Use attention mechanisms to enhance the model's sensitivity to key emotional vocabulary while reducing interference from irrelevant information.

[0039] 2. Voice Sentiment Analysis Algorithm - Based on audio signals, use deep neural networks such as convolutional neural networks (CNN) combined with recurrent neural networks (RNN) or gated recurrent units (GRU) for emotion recognition.

[0040] - In addition to directly analyzing the tone, pitch, and speed of human speech, combine the rhythm, melody, and timbre of background music to comprehensively judge the emotional color of the speaker or music.

[0041] - Introduce time series data processing to capture the trend and pattern of emotional fluctuations, improving the accuracy of dynamic emotional changes.

[0042] 3. Bimodal Fusion - After obtaining the text and voice emotion labels, use multi-modal fusion techniques such as joint encoding of neural networks or deep fusion networks to integrate information from both modalities.

[0043] - During the fusion process, the algorithm learns which modality is more decisive in which situation, dynamically adjusting the weights of the two to achieve the optimal emotional judgment.

[0044] - Introduce a cross-modal consistency loss function to ensure that the text and voice emotion labels are logically consistent, further improving the credibility of the results. Implementation

[0045] - Data preprocessing: standardize and extract features from input text and audio data to ensure consistent data format and dimensionality between the two modalities, facilitating subsequent fusion processing.

[0046] - Feature extraction and representation: For text, use word embeddings (e.g., Word2Vec or GloVe) to capture semantic information; for sound, extract audio features such as Mel-frequency cepstral coefficients (MFCCs), zero-crossing rate, vocal cord pitch, etc.

[0047] - Fusion model training: Train the fusion model using a composite dataset containing both text and sound emotion annotations. The dataset should cover a wide range of emotion types and expressions to ensure the model's generalization ability.

[0048] - Dynamic adjustment and optimization: Based on real-time feedback, dynamically adjust parameters in the algorithm, including the weights of text and sound modalities, the strategy of fusion methods, etc., to adapt to changing contexts and users.

[0049] Furthermore, the real-time audio processing unit employs a short-time Fourier transform-based bass drum frequency detection algorithm to accurately detect specific drum hits in the audio signal. Technically, the short-time Fourier transform algorithm is optimized for real-time processing needs. In principle, the algorithm identifies the characteristic frequencies of bass drums and military drums by decomposing the frequency spectrum of the audio signal. In terms of effects, the technology in this embodiment can accurately capture drum hits in music, enabling immediate response of the lighting. In other embodiments, more complex feature extraction techniques such as deep learning convolutional neural networks can be introduced to improve the accuracy and stability of drum hit detection, addressing the challenge of drum hit recognition in noisy environments.

[0050] Furthermore, the deep learning model uses a pre-trained neural network to analyze the audio stream online and generate lighting programming strategies in real time. Technically, the deep learning model has strong online learning and adaptation capabilities. In principle, the pre-trained neural network learns from a large number of music samples and can analyze real-time audio streams online to predict the best lighting effects. In terms of effects, the technology in this embodiment can dynamically adjust the lighting according to the changes in music, creating a more lively and interactive performance experience. In other embodiments, regular updates to the model training data can be used to continuously optimize model performance, addressing the adaptability issues caused by changes in music styles over time.

[0051] Further, the multi-channel input function supports simultaneous access to a maximum of 16 independent sound sources, each channel equipped with an independent audio processor, ensuring the accuracy of multi-source synchronous processing. Technically, the system adopts a distributed audio processing architecture. In principle, each channel's audio processor works independently, coordinated by a central controller, achieving synchronized analysis and processing of multiple sound sources. In effect, the technology in this embodiment can handle complex multi-source scenarios, providing more rich and delicate lighting effects for large-scale performances. In other embodiments, the system's concurrent processing capacity can be improved by increasing the number of processors and optimizing load balancing algorithms, solving the processing bottleneck caused by excessive sound sources in extreme conditions.

[0052] Further, the lighting material library management system contains preset lighting effect templates and an intelligent recommendation system based on machine learning, which can automatically match and optimize lighting materials based on music type. Technically, the intelligent recommendation system is based on music characteristics and historical user preferences. In principle, the system recommends the most suitable lighting effects by analyzing music type and structure, combined with user feedback. In effect, the technology in this embodiment can automatically adjust lighting effects, reducing manual intervention by lighting designers and improving work efficiency. In other embodiments, user personalization settings such as allowing lighting designers to customize recommendation weights can be introduced to further enhance the customization level of lighting effects, solving the problem of large differences in lighting effect requirements in different performance scenarios.

[0053] Further, the Precision Time Protocol (PTP) implements high-precision clock synchronization based on IEEE 1588 standards to achieve sub-millisecond synchronization accuracy between systems. Technically, the PTP protocol provides a strict clock synchronization mechanism. In principle, by sending and receiving timestamps over the network, the system clock deviation is calculated and adjusted to achieve high-precision synchronization. In effect, the technology in this embodiment can ensure perfect synchronization of all audio-visual elements, improving the professionalism of the performance. In other embodiments, network redundancy of the PTP protocol can be enhanced, such as using dual network interfaces, to improve the system's anti-interference ability, solving the problem of decreased synchronization accuracy in unstable network conditions.

[0054] Further, the ART-NET DMX512 module supports data transmission rate up to 100Mbps through the network adapter, ensuring the fast transmission of DMX512 control signals to lighting equipment. Technically, the high-speed network adapter provides a stable data transmission channel. In principle, the ART-NET DMX512 module utilizes high-speed network transmission protocols such as UDP to achieve lossless transmission of DMX512 data. In terms of effects, the technology in this embodiment can ensure the immediacy and integrity of the lighting control signal, avoiding the phenomenon of light flickering or delay. In other embodiments, the data transmission speed and distance can be further improved by upgrading the network hardware, such as using fiber optic networks instead of copper cables, to solve the problem of signal attenuation and delay in large-scale performance venues.

[0055] Further, the emotion analysis algorithm adopts a model combining convolutional neural network (CNN) and recurrent neural network (RNN), effectively analyzing the emotional dimensions of music such as happiness, sadness, tension, etc., and mapping them to corresponding lighting colors and dynamic effects. Technically, CNN is used to extract local features of audio, while RNN is used to capture long-term sequential dependencies. In principle, the model learns the correlation between music emotion and lighting effects through deep learning technology. In terms of effects, the technology in this embodiment can make the lighting effects more delicate and expressive, enhancing the emotional experience of the audience. In other embodiments, the performance of the model can be optimized by introducing an attention mechanism (Attention Mechanism) to solve the problem of emotional recognition accuracy when processing long audio segments.

[0056] Working process or use process In the working process of the intelligent music synchronization light control system of the present application, first, the audio signal is collected in real time through the sound card module, the signal is detected for the bottom drum and military drum hitting time point by the real-time audio processing unit, and the detection result is transmitted to the deep learning model on the embedded mainboard for music feature analysis. Then, the intelligent light controller retrieves, loads and caches the corresponding light materials from the light material library management system according to the analysis result, and at the same time, adopts the precision time protocol (PTP) to align the time code with the stage sound and other control systems, to ensure the consistency and synchronization of all audio-visual elements. The control instructions generated by the embedded mainboard are transmitted to the relay control board through the single-chip mainboard, to control the switching of the 220V light equipment, and at the same time, the DMX512 control signal is transmitted to the stage lamp through the ART-NET DMX512 module, to realize the dynamic arrangement of the light. In the whole process, the emotion analysis algorithm converts the emotional changes of the music into the color and brightness changes of the light, to further improve the artistic expressiveness of the light effect. The multi-channel input function enables the system to simultaneously process multiple sound sources, to realize seamless switching and coordination of the light effect among multiple sound sources through intelligent coordination. The system finally connects with the mainstream stage light control system in the market through various network control protocols such as Art-Net, to ensure the wide applicability and high compatibility of the system. In the whole working process, the system not only realizes the precise synchronization of music and light, but also significantly improves the audio-visual experience and artistic appeal of the performance through intelligent light material management and dynamic arrangement.

[0057] The systems, apparatuses, modules or units illustrated by the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions.

[0058] It should be further understood that the terms “including”, “containing” or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or apparatus. Without more limitations, the element defined by the statement “including a…” does not exclude the presence of additional identical elements in the process, method, article or apparatus including the element.

[0059] The above embodiments should be understood as merely illustrating the present application and not limiting the protection scope of the present application. After reading the content disclosed in the present application, those skilled in the art can make various modifications or changes to the present application, and these equivalent changes and modifications also fall within the scope defined by the claims of the present application.

Claims

1. An intelligent rhythmic lighting control system, characterized in that: include: The intelligent lighting controller is used to download the sound feature driver file from the cloud and generate control instructions based on the file and the material clip file produced by the lighting engineer; the sound feature driver file is generated by the AI ​​model based on the input audio data and the corresponding sound feature driver file is stored in the cloud; the relay control board is connected to the intelligent lighting controller and is used to receive the control instructions and control the switching of the 220V lighting equipment; the switch module is connected to the intelligent lighting controller and is used to realize data exchange within the system; An embedded motherboard, which is mounted on the intelligent lighting controller and is used to execute software for music feature recognition and lighting programming; A single-chip computer mainboard, connected to the relay control board and the external device, for receiving control instructions from the intelligent lighting controller and controlling the actions of the relay control board and the external device; A sound card module, connected to the intelligent lighting controller, for sampling audio signals in real time and transmitting the audio signals to the embedded mainboard; an ART-NET DMX512 module, which transmits DMX512 control signals to stage lamps based on the Art-Net protocol, and is connected to the intelligent lighting controller.

2. The intelligent rhythmic lighting control system according to claim 1, characterized in that: The sound card module includes a real-time audio processing unit that detects the kick drum / snare drum hitting time points in the audio with an accuracy of ±10ms, and the detection results are used for deep feature analysis.

3. The intelligent rhythmic lighting control system according to claim 1, characterized in that: The embedded motherboard is equipped with a deep learning model to automatically generate lighting choreography strategies adapted to the music style, including color gradient, strobe and intensity adjustment.

4. The intelligent rhythmic lighting control system according to claim 1, characterized in that: The sound card module supports multi-channel input, processes multiple audio source signals in real time, and realizes the coordination of multiple audio source lighting effects.

5. The intelligent rhythmic lighting control system according to claim 1, characterized in that: The intelligent lighting controller is provided with a lighting material library management system, which retrieves and caches lighting materials that meet the characteristics of music in real time.

6. An intelligent rhythmic lighting control system according to any one of claims 1 to 5, characterized in that: The intelligent lighting controller uses the Precision Time Protocol (PTP) to ensure consistent synchronization of audio-visual elements.

7. An intelligent rhythmic lighting control system according to any one of claim 1, characterized in that: The ART-NET DMX512 module is compatible with the Art-Net protocol.

8. The intelligent rhythmic lighting control system according to claim 1, characterized in that: The embedded mainboard uses an emotion analysis algorithm to convert music emotions into light color and brightness changes in real time.

9. The intelligent rhythmic lighting control system according to claim 1, characterized in that: The real-time audio processing unit uses a short-time Fourier transform algorithm to accurately detect specific drum beat frequencies.

10. The intelligent rhythmic lighting control system according to claim 8, characterized in that: The sentiment analysis algorithm uses a dual-modal sentiment analysis algorithm that combines sound and text. After obtaining the emotion labels of text and sound, it uses multimodal fusion technology and joint encoding of neural networks to integrate the information of the two modalities. During the fusion process, the weights of the two are dynamically adjusted to obtain the optimal emotional judgment.

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