Method, system, storage medium and program product for controlling rhythmic flashing of LED light strips

Abstract

A method, system, storage medium, and program product for controlling the rhythmic flashing of an LED light strip are disclosed. The method involves: calculating the relative entropy values ​​between high-frequency, mid-frequency, and low-frequency bands; establishing an energy flow diagram and determining the dominant frequency band based on the topological structure of the energy flow in the diagram; constructing a phase tracking function and classifying the beat markers into hierarchical levels; establishing a recursive segmentation window, calculating the spectral centroid trajectory, and extracting the inflection point sequence; establishing the correspondence between polar coordinate angles and LED light strip color parameters, and the correspondence between polar radius and brightness parameters; designing the spatial propagation mode of the LED light strip based on the topological structure of the energy flow diagram, ensuring that the luminous efficacy along the propagation direction of the LED light strip aligns with the energy flow direction between frequency bands, and adjusting the propagation speed according to the energy flow intensity; and generating a driving control sequence for the LED light strip based on a gradient function. The method also captures the emotional progression and tension changes in music and accurately controls the rhythmic flashing of the LED light strip based on these changes.

Description

Technical Field

[0001] This application belongs to the field of lighting control, and in particular relates to a control method, system, storage medium and program product for rhythmic flashing of LED light strips. Background Technology

[0002] As people's pursuit of rhythmic music continues to increase, synchronizing music rhythm with LED lighting has become an important means of creating atmosphere and enhancing audiovisual experience. Traditional LED light strips can usually only be controlled by simple on / off switches or preset flashing patterns, and cannot be synchronized with the rhythm and emotional expression of music in real time. This results in monotonous lighting effects, a lack of interactivity, and an inability to meet users' needs for an immersive music experience.

[0003] Currently, some audio control systems control the brightness and color of LED light strips by collecting changes in the amplitude of audio signals, achieving basic synchronization between light and music. These systems use Fourier transform to perform spectral analysis on the audio signal, mapping the energy distribution of different frequency bands onto various parameters of the LED light strip, allowing the lighting effects to change accordingly with the rhythm of the music.

[0004] However, existing audio control systems often focus on the amplitude characteristics of audio signals when processing complex music, and do not adequately reflect the rhythmic structure and emotional features of the music. As a result, although the rhythmic effect of the LED light strip can follow the changes in the music, it lacks a deep understanding of the inherent laws of music and is difficult to accurately capture and express the emotional progression and tension changes in the music. Summary of the Invention

[0005] This application provides a method, system, storage medium, and program product for controlling the rhythmic flashing of an LED light strip, used to capture the emotional progression and tension changes of music, and accurately control the rhythmic flashing of the LED light strip according to the emotional progression and tension changes.

[0006] In the first aspect, this application provides a method for controlling the rhythmic flashing of an LED light strip, which decomposes the acquired audio data to obtain the energy distribution matrix of high frequency band, mid frequency band and low frequency band, and calculates the relative entropy value between each frequency band, including high-mid entropy value, high-low entropy value and mid-low entropy value.

[0007] An energy flow map is established based on the trend of relative entropy, and the dominant frequency band is determined according to the topological structure of energy flow in the energy flow map. The energy flow map represents the direction and intensity of energy transfer between adjacent frequency bands.

[0008] A phase tracking function is constructed within the dominant frequency band. The beat markers in the audio signal are located based on the phase tracking function, and the beat markers are classified into different levels according to the morphological characteristics of the phase envelope.

[0009] A recursive segmentation window is constructed with the beat marker point as the boundary. The spectral centroid trajectory within each recursive segmentation window is calculated, and the inflection point sequence of the spectral centroid trajectory is extracted. The inflection point sequence reflects the emotional turning point of the audio signal.

[0010] The inflection point sequence is mapped to the polar coordinate system to establish the correspondence between polar coordinate angles and LED light strip color parameters, and the correspondence between polar diameter and brightness parameters.

[0011] The spatial propagation mode of LED light strips is designed based on the topology of the energy flow diagram, so that the luminous efficacy along the propagation direction of the LED light strip is consistent with the energy flow direction between frequency bands, and the propagation speed is adjusted according to the energy flow intensity.

[0012] The visual parameters between adjacent beat markers are smoothed to construct a gradient function that includes the inertial characteristics of the audio signal. Based on the gradient function, a driving control sequence for the LED light strip is generated so that the LED light strip performs corresponding color and brightness changes according to the driving control sequence.

[0013] By employing the aforementioned technical solutions, multi-band decomposition and relative entropy calculation of audio data accurately capture the energy distribution relationship between different frequency bands. Based on the topological structure of the energy flow graph, the dominant frequency band is determined, making subsequent beat detection more targeted. By constructing a phase tracking function within the dominant frequency band and combining it with hierarchical analysis of phase envelope features, the accuracy of beat marker location is improved. The recursive segmentation window and spectral centroid trajectory analysis method, built based on beat markers, can accurately extract emotional transition moments in the audio signal. Mapping the inflection point sequence to a polar coordinate system establishes a correspondence with LED light strip parameters, achieving a natural transition from audio emotional features to visual effects. The spatial propagation mode designed based on the energy flow graph ensures that the visual effects of the LED light strip remain synchronized with the energy flow characteristics of the audio, enhancing visual expressiveness. Through smoothing visual parameters and constructing a gradient function, the color and brightness changes of the LED light strip reflect the rhythm of the audio while maintaining the continuity of movement, avoiding abrupt changes and improving the overall smoothness of the visual experience. It captures the emotional progression and tension changes in music, and accurately controls the LED light strip to flash rhythmically according to the emotional progression and tension changes.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the acquired audio data is decomposed, specifically including:

[0015] The audio data is processed by overlapping frames, with the overlap rate between adjacent frames being a preset value, and a Hanning window is added to each frame of audio data.

[0016] Wavelet packet decomposition is performed on each windowed audio data frame to obtain frequency subband coefficients;

[0017] Based on the characteristics of human hearing, the frequency sub-band coefficients obtained by wavelet packet decomposition are reorganized into high-frequency, mid-frequency, and low-frequency bands, and the energy distribution matrix of each frequency band is calculated.

[0018] The relative entropy values ​​between high and mid frequency bands, high and low frequency bands, and mid and low frequency bands are calculated based on the information entropy theory. The relative entropy values ​​are obtained by calculating the probability density function of the energy distribution of each frequency band.

[0019] By adopting the above technical solution and setting a processing method for overlapping and framing audio data, the continuity of information between adjacent frames is ensured, avoiding energy leakage caused by signal abrupt changes. Wavelet packet decomposition is performed on the windowed audio data of each frame, achieving fine division of the signal in the time and frequency domains. The frequency sub-band coefficients are reorganized according to the characteristics of human hearing, making the frequency band division more consistent with the laws of human auditory perception. The relative entropy value is calculated using information entropy theory, and the similarity of energy distribution between frequency bands is characterized by probability density function, providing a reliable mathematical basis for the extraction of energy flow features. This scheme improves the rationality of frequency band division and the accuracy of energy feature extraction while ensuring computational efficiency, making subsequent beat detection and visual mapping more consistent with human perception characteristics.

[0020] In conjunction with some embodiments of the first aspect, in some embodiments, the step of establishing an energy flow map based on the trend of relative entropy value changes specifically includes:

[0021] Calculate the rate of change of relative entropy values ​​of each frequency band between adjacent time frames to construct a three-dimensional energy flow vector field;

[0022] Topological analysis of the three-dimensional energy flow vector field is performed to identify energy convergence and divergence points;

[0023] Based on the spatial distribution characteristics of energy convergence and divergence points, an energy transfer network is constructed;

[0024] Dominant frequency bands are determined based on connectivity analysis of energy transfer networks, and these dominant frequency bands have the largest energy inflow-outflow ratio.

[0025] By employing the aforementioned technical solution, the rate of change of relative entropy values ​​for each frequency band between adjacent time frames was calculated, constructing a three-dimensional vector field capable of characterizing dynamic energy changes. Topological analysis of the vector field identified energy convergence and divergence points, revealing key nodes in energy transfer within the audio signal. An energy transfer network constructed based on spatial distribution characteristics reflects the overall characteristics of energy flow between frequency bands. Connectivity analysis of the energy transfer network determined the dominant frequency band, ensuring that the selected dominant frequency band possesses the most significant energy transfer characteristics. This energy topology-based analysis method makes the selection of dominant frequency bands more objective and reliable, providing a more stable feature foundation for subsequent beat detection and improving the system's adaptability to different types of audio signals.

[0026] In conjunction with some embodiments of the first aspect, in some embodiments, before constructing the phase tracking function within the dominant frequency band, the method further includes:

[0027] Adaptive threshold segmentation is performed on the energy distribution of the dominant frequency band to extract energy peak points;

[0028] Calculate the time interval sequence between adjacent energy peak points;

[0029] Perform periodic analysis on the time interval sequence to determine the basic beat period;

[0030] The time window length of the phase tracking function is set according to the basic beat cycle.

[0031] By employing the above technical solution, adaptive threshold segmentation of the energy distribution in the dominant frequency band can be performed, automatically adjusting the threshold according to signal characteristics and improving the robustness of energy peak point extraction. A temporal relationship model of the beat was established by analyzing the time interval sequence between adjacent energy peak points. Periodic analysis of the time interval sequence accurately extracted the basic beat characteristics of the audio signal. The time window length of the phase tracking function was set according to the basic beat period, ensuring that the temporal resolution of phase tracking matches the rhythmic characteristics of the audio signal. This solution, through in-depth analysis of energy distribution characteristics, established the correlation between the beat period and phase tracking parameters, improving the targeting and accuracy of phase tracking, and making subsequent beat marker location more precise and reliable.

[0032] In conjunction with some embodiments of the first aspect, in some embodiments, a phase tracking function is constructed within the dominant frequency band, specifically including:

[0033] Perform complex wavelet transform on the signal in the dominant frequency band to obtain phase information in the time-frequency plane;

[0034] Calculate the time derivative of the phase information to obtain the instantaneous frequency curve;

[0035] Detect abrupt changes in the instantaneous frequency curve and mark the locations of these abrupt changes as candidate beat points;

[0036] The morphological features of the phase envelope are analyzed, and the candidate beat points are hierarchically divided according to the morphological features, including the steepness, width and symmetry of the phase envelope.

[0037] Construct a weight function based on the hierarchical information of the candidate beat points;

[0038] The phase information is convolved with the weight function to obtain the enhanced phase features.

[0039] A phase tracking function is constructed based on the enhanced phase features.

[0040] By employing the aforementioned technical solution, complex wavelet transform is performed on the dominant frequency band signal to obtain complete phase information in the time-frequency plane, preserving the signal's time-frequency characteristics. The instantaneous frequency curve is obtained by calculating the time derivative of the phase information, reflecting the dynamic changes in signal frequency. By detecting abrupt changes in the instantaneous frequency curve as candidate beat points, and combining this with morphological features such as the steepness, width, and symmetry of the phase envelope for hierarchical classification, precise quantification of beat strength is achieved. A weighting function is constructed based on the hierarchical information of the candidate beat points and convolved with the phase information, enhancing the phase characteristics of the true beat points while suppressing the influence of noise and false beat points. The final phase tracking function comprehensively considers the signal's time-frequency characteristics, beat hierarchy, and phase enhancement effect, improving the accuracy and robustness of beat detection and enabling the system to adapt to changes in different music styles and rhythmic characteristics.

[0041] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes, before constructing the recursive segmentation window with the beat marker point as the boundary:

[0042] Calculate the time distribution density of each level of beat points;

[0043] A decision function for adaptive window size is constructed based on the time distribution density;

[0044] Adjust the depth of recursive segmentation based on the decision function;

[0045] Establish a hierarchical structure tree for recursive partitioning.

[0046] By employing the aforementioned technical solution, the temporal distribution density of beat points at each level was calculated, revealing the rhythmic variation patterns of the audio signal over time. A decision function for adaptive window size was constructed based on the temporal distribution density, allowing the size of the recursive segmentation window to automatically adjust according to the temporal characteristics of the beat. The decision function dynamically adjusts the depth of recursive segmentation, avoiding over-segmentation or under-segmentation. The established recursive segmentation hierarchy structure tree clearly expresses the hierarchical temporal structure of the audio signal, enabling the system to automatically select appropriate analysis granularity based on the complexity of the music, thus improving the accuracy and computational efficiency of spectral centroid trajectory analysis. This adaptive recursive segmentation scheme enhances the system's ability to analyze the temporal structure of audio signals, making the extraction of emotional transition moments more accurate and reliable.

[0047] In conjunction with some embodiments of the first aspect, in some embodiments, generating the driving control sequence for the LED light strip based on the gradient function specifically includes:

[0048] Calculate intermediate state parameters between adjacent beat points based on the gradient function;

[0049] The sequence of intermediate state parameters is spatially mapped according to the physical distribution of the LED strip to obtain the spatial mapping result;

[0050] Calculate the propagation delay parameters based on the energy flow intensity;

[0051] Integrate spatial mapping results and propagation delay parameters to generate a time-sequential drive control sequence.

[0052] By employing the aforementioned technical solution, and calculating intermediate state parameters between adjacent beat points based on a gradient function, a smooth transition in the visual effect of the LED light strip is achieved. The sequence of intermediate state parameters is spatially mapped according to the physical distribution of the LED light strip, ensuring the continuity and coordination of the visual effect in the spatial dimension. The propagation delay parameter is calculated based on the energy flow intensity, allowing the propagation speed of the light effect to accurately reflect the energy change characteristics of the audio signal. By integrating the spatial mapping results and the propagation delay parameters to generate a time-sequential drive control sequence, unified control of the LED light strip in both time and space dimensions is achieved. This ensures that the visual effect accurately expresses the rhythm of the audio while maintaining the continuity and naturalness of the movement, enhancing the overall artistic expressiveness of the visual effect.

[0053] Secondly, embodiments of this application provide a control system for rhythmic flashing of an LED light strip. The control system includes one or more processors and a memory. The memory is coupled to one or more processors and is used to store computer program code, which includes computer instructions. One or more processors invoke the computer instructions to cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0054] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a system, cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0055] Fourthly, embodiments of this application provide a computer program product that, when run on a system, causes the system to execute the method described in any possible implementation of the first aspect.

[0056] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0057] 1. This application provides a method for controlling the rhythmic flashing of an LED light strip. It performs multi-band decomposition and relative entropy calculation on audio data, accurately capturing the energy distribution relationship between different frequency bands. Based on the topological structure of the energy flow graph, it determines the dominant frequency band, making subsequent beat detection more targeted. By constructing a phase tracking function within the dominant frequency band and combining it with hierarchical analysis of phase envelope features, the accuracy of beat marker location is improved. A recursive segmentation window and spectral centroid trajectory analysis method based on beat markers can accurately extract emotional transition moments in the audio signal. Mapping the inflection point sequence to a polar coordinate system establishes a correspondence with the LED light strip parameters, achieving a natural transition from audio emotional features to visual effects. The spatial propagation mode designed based on the energy flow graph allows the visual effect of the LED light strip to remain synchronized with the energy flow characteristics of the audio, enhancing visual expressiveness. Through smoothing the visual parameters and constructing a gradient function, the color and brightness changes of the LED light strip reflect the rhythm of the audio while maintaining the continuity of movement, avoiding abrupt changes and improving the overall smoothness of the visual experience. It captures the emotional progression and tension changes in music, and accurately controls the LED light strip to flash rhythmically according to the emotional progression and tension changes.

[0058] 2. This application provides a control method for the rhythmic flashing of LED light strips. It employs adaptive threshold segmentation of the energy distribution in the dominant frequency band, automatically adjusting the threshold based on signal characteristics to improve the robustness of energy peak point extraction. By analyzing the time interval sequence between adjacent energy peak points, a temporal relationship model of the beat is established. Periodic analysis of the time interval sequence accurately extracts the basic beat characteristics of the audio signal. The time window length of the phase tracking function is set according to the basic beat period, ensuring that the temporal resolution of the phase tracking matches the rhythmic characteristics of the audio signal. This scheme, through in-depth analysis of energy distribution characteristics, establishes a correlation between the beat period and phase tracking parameters, improving the targeting and accuracy of phase tracking, and making subsequent beat marker positioning more precise and reliable.

[0059] 3. This application provides a method for controlling the rhythmic flashing of LED light strips, calculating the temporal distribution density of each level of beat points, and understanding the rhythmic variation patterns of audio signals in the time dimension. Based on the temporal distribution density, an adaptive window size decision function is constructed, allowing the size of the recursive segmentation window to automatically adjust according to the temporal characteristics of the beat. The depth of recursive segmentation is dynamically adjusted through the decision function, avoiding over-segmentation or under-segmentation. The established recursive segmentation hierarchy structure tree clearly expresses the hierarchical temporal structure of the audio signal, enabling the system to automatically select an appropriate analysis granularity based on the complexity of the music, improving the accuracy and computational efficiency of spectral centroid trajectory analysis. This adaptive recursive segmentation scheme enhances the system's ability to analyze the temporal structure of audio signals, making the extraction of emotional transition moments more accurate and reliable. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating a method for controlling the rhythmic flashing of an LED light strip in an embodiment of this application.

[0061] Figure 2 This is another flowchart illustrating a method for controlling the rhythmic flashing of an LED light strip in an embodiment of this application.

[0062] Figure 3 This is a schematic diagram of the physical device structure of a control system for rhythmic flashing of an LED light strip provided in an embodiment of this application. Detailed Implementation

[0063] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0064] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0065] The following is combined Figure 1 The present application describes a method for controlling the rhythmic flashing of an LED light strip in its embodiments:

[0066] Please see Figure 1 This is a flowchart illustrating a method for controlling the rhythmic flashing of an LED light strip in an embodiment of this application.

[0067] S101. Decompose the acquired audio data to obtain the energy distribution matrices of the high-frequency band, mid-frequency band, and low-frequency band, and calculate the relative entropy values ​​between each frequency band.

[0068] The system decomposes the acquired audio data to obtain energy distribution matrices for high-frequency, mid-frequency, and low-frequency bands. Specifically, the audio data is processed by overlapping and framing, with the overlap rate between adjacent frames set to a preset value, and a Hanning window is added to each frame of audio data.

[0069] Wavelet packet decomposition is performed on each windowed audio data frame to obtain frequency subband coefficients;

[0070] Based on the characteristics of human hearing, the frequency sub-band coefficients obtained by wavelet packet decomposition are reorganized into high-frequency, mid-frequency, and low-frequency bands, and the energy distribution matrix of each frequency band is calculated.

[0071] The relative entropy values ​​between high and mid frequency bands, high and low frequency bands, and mid and low frequency bands are calculated based on the information entropy theory. The relative entropy values ​​are obtained by calculating the probability density function of the energy distribution of each frequency band.

[0072] Calculate the relative entropy values ​​between each frequency band, including high-medium entropy, high-low entropy, and medium-low entropy.

[0073] The main purpose of this step is to perform frequency domain decomposition on the audio data, obtain the energy distribution characteristics of different frequency bands, and calculate the relative entropy values ​​between frequency bands for subsequent analysis of the frequency domain structure and energy flow patterns of the audio signal. The system first preprocesses the acquired audio data, such as removing DC components and normalizing, to improve signal quality and stability. Then, the system segments the audio data using an overlapping framing method, adding a Hanning window to each frame to reduce spectral leakage. Next, the system performs wavelet packet decomposition on each windowed frame of audio data, dividing the signal into multiple frequency sub-bands and calculating the frequency coefficients of each sub-band. Based on the auditory characteristics of the human ear, the system reassembles the frequency sub-bands obtained from wavelet packet decomposition into high-frequency, mid-frequency, and low-frequency bands, and calculates the energy distribution matrix for each band. Finally, the system calculates the relative entropy values ​​between the high-mid frequency band, the high-low frequency band, and the mid-low frequency band based on information entropy theory. The relative entropy values ​​are calculated using the probability density function of the energy distribution of each frequency band.

[0074] During frequency domain decomposition, the system can employ different time-frequency analysis methods, such as Short-Time Fourier Transform (STFT) and Wavelet Transform (WT), selecting an appropriate decomposition strategy based on the characteristics of the audio signal. For wavelet packet decomposition, the system can choose different wavelet basis functions, such as Daubechies wavelet and Symlets wavelet, to optimize time-frequency resolution. When calculating the energy distribution matrix, the system can introduce perceptual weighting functions, such as psychoacoustic models, to better reflect the auditory perception characteristics of the human ear. Simultaneously, the system can design adaptive frequency band partitioning strategies, dynamically adjusting the boundaries of high, mid, and low frequency bands according to the spectral structure of the audio signal, improving the flexibility and adaptability of frequency band partitioning.

[0075] Furthermore, the system can incorporate a time smoothing mechanism when calculating relative entropy, applying a moving average or weighted average to the relative entropy sequence to suppress the effects of noise and transient fluctuations. For complex audio scenarios, such as multi-channel and stereo, the system can perform frequency domain decomposition and relative entropy calculation for each channel separately, and comprehensively analyze the interrelationships and energy distribution differences between channels to obtain more comprehensive and accurate frequency domain characteristics. In summary, by performing frequency domain decomposition and relative entropy calculation on audio data, the system can deeply analyze the frequency domain structure and energy flow patterns of audio signals, providing crucial feature information for subsequent steps such as rhythm analysis and visual mapping.

[0076] S102. Establish an energy flow diagram based on the changing trend of relative entropy, and determine the dominant frequency band based on the topological structure of energy flow in the energy flow diagram;

[0077] The system establishes an energy flow map based on the changing trend of relative entropy values. Specifically, it calculates the rate of change of relative entropy values ​​in each frequency band between adjacent time frames and constructs a three-dimensional energy flow vector field.

[0078] Topological analysis of the three-dimensional energy flow vector field is performed to identify energy convergence and divergence points;

[0079] Based on the spatial distribution characteristics of energy convergence and divergence points, an energy transfer network is constructed;

[0080] Dominant frequency bands are determined based on connectivity analysis of energy transfer networks, and these dominant frequency bands have the largest energy inflow-outflow ratio.

[0081] The dominant frequency band is determined based on the topological structure of energy flow in the energy flow diagram. The energy flow diagram represents the direction and intensity of energy transfer between adjacent frequency bands.

[0082] The main purpose of this step is to establish an energy flow map based on the changing trend of relative entropy values, characterize the energy transfer and interaction patterns of audio signals between different frequency bands, and further analyze the topological structure of the energy flow map to determine the dominant frequency band in the entire audio segment. The system first calculates the rate of change of relative entropy values ​​for each frequency band between adjacent time frames, obtaining a three-dimensional vector field reflecting the direction and intensity of energy flow between frequency bands. Then, the system performs topological analysis on the three-dimensional energy flow vector field, identifying energy convergence points (sources) and divergence points (sinks), characterizing the inflow and outflow characteristics of energy between frequency bands. Next, based on the spatial distribution characteristics of energy convergence and divergence points, the system constructs an energy transfer network between frequency bands, revealing the energy flow topology of the audio signal in the frequency domain. Finally, the system performs connectivity analysis on the energy transfer network, calculates the energy inflow and outflow ratio for each frequency band, and determines the frequency band with the largest energy inflow and outflow ratio as the dominant frequency band.

[0083] When constructing energy flow maps, the system can employ various mathematical models and computational methods, such as vector field topology analysis and graph theory algorithms, to accurately and efficiently characterize the energy transfer patterns between frequency bands. For complex audio signals, the system can introduce a multi-scale analysis strategy to construct energy flow maps at different time scales, capturing the energy flow characteristics of the audio signal at different temporal resolutions. Simultaneously, the system can design an adaptive topology recognition algorithm that dynamically adjusts the thresholds for identifying energy convergence and divergence points based on the distribution characteristics of the energy flow vector field, improving the robustness and adaptability of topology analysis.

[0084] Furthermore, the system can comprehensively consider other musical features, such as rhythm and melody, when determining the dominant frequency band, to more fully assess the importance and dominance of the frequency band within the entire audio segment. For musical works containing multiple themes or sections, the system can employ a segmented analysis strategy, independently constructing energy flow maps and determining the dominant frequency band within each section. By comparing and integrating the sections, the system can derive the distribution pattern of the dominant frequency band for the entire piece. In summary, by constructing energy flow maps and analyzing their topological structure, the system can deeply explore the patterns of energy transfer and interaction in the frequency domain of audio signals, providing important decision-making basis for applications such as music rhythm analysis and emotional expression. It has significant theoretical and practical value for fields such as music information retrieval and audio analysis.

[0085] S103. Construct a phase tracking function within the dominant frequency band, locate the beat markers in the audio signal based on the phase tracking function, and classify the beat markers into hierarchical levels based on the morphological characteristics of the phase envelope.

[0086] The system constructs a phase tracking function within the dominant frequency band. Specifically, it performs a complex wavelet transform on the signal in the dominant frequency band to obtain phase information in the time-frequency plane.

[0087] Calculate the time derivative of the phase information to obtain the instantaneous frequency curve;

[0088] Detect abrupt changes in the instantaneous frequency curve and mark the locations of these abrupt changes as candidate beat points;

[0089] The morphological features of the phase envelope are analyzed, and the candidate beat points are hierarchically divided according to the morphological features, including the steepness, width and symmetry of the phase envelope.

[0090] Construct a weight function based on the hierarchical information of the candidate beat points;

[0091] The phase information is convolved with the weight function to obtain the enhanced phase features.

[0092] A phase tracking function is constructed based on the enhanced phase features.

[0093] The beat markers in the audio signal are located using the phase tracking function, and the beat markers are classified into different levels according to the morphological characteristics of the phase envelope.

[0094] The main purpose of this step is to construct a phase tracking function within the dominant frequency band. By analyzing the phase information of the audio signal, it achieves precise localization and hierarchical classification of beat markers, providing rhythmic structure information for subsequent visual mapping and lighting control. The system first performs a complex wavelet transform on the signal in the dominant frequency band to obtain the phase information of the audio signal in the time-frequency plane. Then, the system calculates the derivative of the phase information with respect to time, obtaining an instantaneous frequency curve reflecting the frequency change trend. By detecting abrupt changes in the instantaneous frequency curve, the system can identify candidate beat points in the audio signal. Next, the system analyzes the morphological characteristics of the phase envelope near the candidate beat points, such as steepness, width, and symmetry, and accordingly classifies the candidate beat points into hierarchical levels, obtaining marker information reflecting the beat structure hierarchy. Finally, the system constructs a weighting function based on the hierarchical information of the beat markers, performs convolution operations between the phase information and the weighting function to obtain enhanced phase features, and constructs a phase tracking function based on the enhanced phase features to achieve precise localization and tracking of the beat markers.

[0095] When constructing the phase tracking function, the system can employ various time-frequency analysis tools, such as the Hilbert-Huang transform and synchronous compression transform, to extract instantaneous phase information from the audio signal. For complex music signals, the system can introduce multi-scale, multi-resolution phase analysis strategies. By extracting and fusing phase information at different time and frequency scales, the accuracy and robustness of beat marker localization can be improved. Simultaneously, the system can design adaptive phase envelope morphological feature extraction and classification algorithms. Based on prior knowledge such as music style and rhythm type, the system dynamically adjusts the morphological feature judgment threshold and classification strategy to achieve intelligent and personalized hierarchical division of beat markers.

[0096] Furthermore, the system can utilize music theory and rules, such as the patterns of beat strength and measure structure, to correct and optimize the hierarchical classification of beat markers, improving the rationality and accuracy of the musical division. When tracking beat markers, the system employs an adaptive time window strategy, dynamically adjusting the analysis window size of the phase tracking function based on the density and variation of the musical rhythm to balance time and frequency resolution, achieving precise positioning and real-time tracking of beat markers. In summary, by constructing a phase tracking function within the dominant frequency band and combining it with phase envelope morphology features to hierarchically classify beat markers, the system can accurately characterize the rhythmic structure and layers of musical signals, providing crucial rhythmic marking information for applications such as music beat synchronization and dance choreography, demonstrating broad application prospects.

[0097] S104. Construct a recursive segmentation window with the beat marker as the boundary, calculate the spectral centroid trajectory within each recursive segmentation window, and extract the inflection point sequence of the spectral centroid trajectory.

[0098] Before this step, the system calculates the time distribution density of each level of beat points;

[0099] A decision function for adaptive window size is constructed based on the time distribution density;

[0100] Adjust the depth of recursive segmentation based on the decision function;

[0101] Establish a hierarchical structure tree for recursive partitioning.

[0102] Then, a recursive segmentation window is constructed with the beat marker point as the boundary, the spectral centroid trajectory within each recursive segmentation window is calculated, and the inflection point sequence of the spectral centroid trajectory is extracted.

[0103] The main purpose of this step is to construct recursive segmentation windows using beat markers as boundaries. By calculating the spectral centroid trajectory and extracting inflection point sequences within each segmentation window, the rhythmic structure and variation patterns of the music signal are further refined and quantified. Before this step, the system first calculates the temporal distribution density of beat points at each level and constructs a decision function for adaptive window size based on the temporal distribution density to determine the optimal time scale and resolution for recursive segmentation. Then, the system dynamically adjusts the depth of recursive segmentation according to the decision function and establishes a recursive segmentation tree that reflects the segmentation levels and temporal relationships. Next, the system constructs corresponding time windows at each node of the recursive segmentation tree, using beat markers as boundaries, and performs spectral analysis on the audio signal within each time window to calculate the centroid trajectory of the energy distribution within that window. By smoothing the spectral centroid trajectory and detecting inflection points, the system can obtain an inflection point sequence that reflects the rhythmic change trend, characterizing the temporal dynamics of the music rhythm at different segmentation levels.

[0104] When constructing the recursive segmentation window, the system can employ various time segmentation strategies, such as equal-interval segmentation and Dynamic Time Warping (DTW), to adapt to the characteristics of different music styles and rhythm types. For calculating the spectral centroid trajectory, the system can select different spectral analysis methods, such as Fourier transform and constant-Q transform, to balance time resolution and frequency resolution. When extracting inflection point sequences, the system can design an adaptive inflection point detection algorithm that dynamically adjusts the threshold and window size for inflection point determination based on the smoothness and rate of change of the spectral centroid trajectory, accurately capturing key moments of rhythmic change. Simultaneously, the system can also introduce multi-scale, multi-resolution inflection point analysis strategies, extracting and fusing inflection point information at different time scales to comprehensively depict the multi-layered structure and variation patterns of musical rhythm.

[0105] Furthermore, the system can utilize music theory and empirical rules to perform musical semantic interpretation and annotation on the extracted inflection point sequences, such as identifying key rhythmic events like time signature changes and syncopation changes. When applied to LED light strip control, the system can map the inflection point sequences to the physical structure and layout of the LED light strips. Based on the time position, amplitude, and other attributes of the inflection points, it generates spatiotemporal coordinates and parameter sequences for the lighting effects, achieving precise synchronization and dynamic mapping between musical rhythm and lighting effects. In summary, by constructing a recursive segmentation window with beat markers as boundaries and extracting the inflection point sequences of the spectral centroid trajectory, the system can analyze the temporal dynamic characteristics of musical rhythm at multiple time scales and resolutions, providing rich rhythmic control information for applications such as music visualization and stage lighting design, demonstrating high practical value and innovation.

[0106] S105. Map the inflection point sequence to the polar coordinate system and establish the correspondence between polar coordinate angles and LED light strip color parameters and the correspondence between polar diameter and brightness parameters.

[0107] The main purpose of this step is to map the extracted inflection point sequence to a polar coordinate system and establish a correspondence between polar coordinate parameters and the visual parameters of the LED light strip, realizing the conversion and mapping of musical rhythm features to lighting effect parameters. First, the system selects a suitable polar coordinate system, such as circular or spiral, based on the physical layout and control requirements of the LED light strip. Then, the system maps the inflection point sequence to the polar coordinate system sequentially according to time, and extracts the polar coordinate angle and polar radius value corresponding to each inflection point. Next, the system establishes a correspondence between the polar coordinate angle and the color parameters of the LED light strip, and a correspondence between the polar radius and the brightness parameters. For color parameters, the system can divide the polar coordinate angle into several intervals and map each interval to a specific color value or color change pattern. For brightness parameters, the system can perform linear or non-linear mapping between the polar radius value and the brightness value to achieve continuous modulation and dynamic changes in brightness. Finally, the system outputs the mapped color and brightness parameter sequences as a command sequence to control the visual effects of the LED light strip.

[0108] When performing polar coordinate mapping of inflection point sequences, the system can employ different mapping functions and interpolation algorithms, such as linear interpolation and spline interpolation, to achieve a smooth transition and continuous mapping between inflection point positions and polar coordinate parameters. For color parameter mapping, the system can utilize color theory and aesthetic principles to design reasonable color schemes and variation patterns, such as complementary color matching and gradient colors, to enhance the artistic expression and emotional impact of the lighting effects. Simultaneously, the system can introduce user interaction and feedback mechanisms, allowing users to customize or adjust color mapping rules to meet personalized visual preferences and creative needs.

[0109] Furthermore, the system can optimize and enhance the polar coordinate mapping results by combining other musical features and contextual information. For example, based on the emotional characteristics and style of the music, the system can dynamically adjust parameters such as saturation and contrast of the color mapping to match the emotional tone and performance style of the music. When controlling LED light strips, the system can comprehensively consider factors such as the physical characteristics, visual effects, and power consumption limitations of the light strips, and perform appropriate scaling and limiting of color and brightness parameters to ensure the stability, consistency, and reliability of the lighting effects. In summary, by mapping the inflection point sequence to the polar coordinate system and establishing the correspondence between polar coordinate parameters and the visual parameters of LED light strips, the system can achieve an intuitive and vivid mapping and transformation of musical rhythm features into lighting effects. This provides a novel and flexible form of expression and interactive method for applications such as music visualization and lighting art, greatly expanding the application scenarios and creative space of LED light strips.

[0110] S106. Design the spatial propagation mode of LED light strip based on the topology structure of the energy flow diagram, so that the luminous efficacy along the propagation direction of the LED light strip is consistent with the energy flow direction between frequency bands, and adjust the propagation speed according to the energy flow intensity.

[0111] The main purpose of this step is to design the spatial propagation mode of the LED light strip based on the topology of the energy flow diagram. By synchronizing and mapping the propagation of light effects with the energy flow between frequency bands, the system achieves a dynamic correlation and representation of the lighting effects and the distribution of music energy. The system first analyzes the topology of the energy flow diagram, extracting key information reflecting the direction and intensity of energy transfer between frequency bands, such as energy flow direction, velocity, and convergence point. Then, based on the physical layout and topology of the LED light strip, the system designs a light effect propagation mode that matches the energy flow diagram, ensuring that the lighting effects along the propagation direction of the LED light strip are consistent with the direction of energy flow between frequency bands. Specifically, the system can divide the LED light strip into several logical regions or control units, and determine the order and priority of light effect propagation in each region according to the energy flow direction. Simultaneously, the system can dynamically adjust the speed and gradation effect of the light effect propagation based on the energy flow intensity, matching the dynamic changes in light effect propagation with the temporal evolution of music energy distribution. Finally, the system generates a spatiotemporal coordinate sequence and parameter sequence to control the propagation of the LED light strip's light effects, and realizes the physical output and display of the lighting effects through a hardware interface and driving circuit.

[0112] When designing the spatial propagation mode of LED light strips, the system can adopt different topologies and mapping strategies, such as linear topology, ring topology, and grid topology, to adapt to different light strip layouts and visual effect requirements. For complex energy flow diagrams, the system can introduce graph theory algorithms and optimization techniques, such as minimum spanning tree and shortest path, to achieve automatic planning and optimization of light effect propagation paths. Simultaneously, the system can design multi-level propagation modes and hierarchical control strategies to achieve coordinated and synchronized light effect propagation at different spatial ranges and temporal granularities based on the hierarchical structure and time scale of energy flow.

[0113] S107. Calculate the deviation between the theoretical concentration change curve and the actual concentration data of the target pollutant.

[0114] The system first extracts visual parameter sequences, such as color, brightness, and position, between adjacent beat markers. Then, it smooths these sequences, eliminating abrupt changes and discontinuities to generate smooth curves or gradient functions. When constructing the gradient function, the system considers the inertial characteristics of the audio signal, such as rhythmic continuity and tempo gradation. By introducing inertial and smoothing terms between adjacent beats, the system matches the changes in visual parameters to the flowing characteristics of the musical rhythm. Next, the system calculates the intermediate state parameters of the LED light strip between adjacent beats based on the gradient function, such as intermediate color and brightness values. Finally, the system spatially maps the intermediate state parameter sequence according to the physical distribution of the LED light strip and calculates the propagation delay and duration of the light effect based on energy flow intensity, generating a complete LED light strip drive control sequence to achieve smooth evolution and dynamic presentation of the lighting effect.

[0115] When smoothing visual parameters, the system can employ different interpolation algorithms and smoothing strategies, such as linear interpolation, spline interpolation, and Gaussian smoothing. The optimal smoothing method and parameter settings are selected based on the characteristics of the music rhythm and the requirements of the lighting effects. For gradient functions that incorporate the inertial characteristics of audio signals, the system can introduce relevant concepts from physics and signal processing, such as moment of inertia, attenuation coefficient, and convolution kernel. By rationally designing mathematical models for the inertial and smoothing terms, the system achieves dynamic synchronization and a natural transition between changes in visual parameters and the music rhythm. Simultaneously, the system can employ adaptive parameter estimation and optimization techniques to adjust the parameters of the gradient function in real time based on the characteristics of music rhythm changes and user feedback, thereby achieving the best visual effects and user experience.

[0116] Furthermore, the system can combine other musical and visual elements to artistically design and personalize the gradient function. For example, based on the emotional characteristics of the music and the meaning of the lyrics, a matching color gradient scheme and animation effect can be designed to enhance the expressiveness and appeal of the lighting effect. When controlling the LED light strip, the system can fully utilize the performance and characteristics of the hardware, such as employing high-precision PWM control and time synchronization mechanisms, to ensure the accuracy, stability, and real-time performance of the lighting effect. In short, by smoothing the visual parameters between adjacent beat markers and constructing a gradient function that incorporates the inertial characteristics of the audio signal, the system can achieve a smooth transition and natural synchronization between the visual effect of the LED light strip and the rhythm of the music, greatly enhancing the artistic expression and interactive experience of the lighting effect. This provides a novel, flexible, and creative technical means and implementation method for applications such as music visualization and lighting art.

[0117] S108. Smooth the visual parameters between adjacent beat markers, construct a gradient function containing the inertial characteristics of the audio signal, and generate the driving control sequence of the LED light strip based on the gradient function.

[0118] The system smooths the visual parameters between adjacent beat markers, constructs a gradient function incorporating the inertial characteristics of the audio signal, and generates a driving control sequence for the LED light strip based on the gradient function. This sequence causes the LED light strip to perform corresponding color and brightness changes. Specifically, generating the driving control sequence for the LED light strip based on the gradient function includes:

[0119] Calculate intermediate state parameters between adjacent beat points based on the gradient function;

[0120] The sequence of intermediate state parameters is spatially mapped according to the physical distribution of the LED strip to obtain the spatial mapping result;

[0121] Calculate the propagation delay parameters based on the energy flow intensity;

[0122] Integrate spatial mapping results and propagation delay parameters to generate a time-sequential drive control sequence.

[0123] The main purpose of this step is to precisely control and synchronize visual parameters. By optimizing the gradient function and feedback mechanism, a high degree of consistency and synchronization between the LED light strip's lighting effects and the music's rhythm is achieved, creating an immersive experience that is highly integrated with the music's emotion and atmosphere. Based on the generated LED light strip drive control sequence, the system precisely controls the color, brightness, and other visual parameters of each LED bead in terms of time and space, ensuring that the changes in the lighting effects are perfectly synchronized with the flow of the music's rhythm. Simultaneously, the system employs high-precision clocks and synchronization mechanisms, such as timestamps and frame synchronization, to ensure coordination and consistency between the LED beads, avoiding visual breaks and jumps. During output control, the system monitors the synchronization and consistency between the music's rhythm and the lighting effects in real time, dynamically adjusting the parameters of the gradient function, such as the inertia coefficient and smoothing factor, through a feedback mechanism to make the lighting effects more natural and fluid, perfectly matching the music's emotion and rhythm. Furthermore, the system can incorporate intelligent algorithms and machine learning technology to adaptively optimize the gradient function and control strategy by analyzing user feedback and interaction data, continuously improving the artistic expression of the lighting effects and the user experience.

[0124] When performing precise control of visual parameters, the system can fully utilize the physical characteristics and performance advantages of LED light strips, such as high dynamic range, wide color gamut, and low power consumption. Through reasonable color management and brightness modulation, it can achieve rich and subtle variations in lighting effects. For large-scale LED light strip arrays, the system can employ a distributed control architecture and parallel processing technology to achieve collaborative work and load balancing among multiple control units, ensuring real-time performance and stability. Simultaneously, the system can integrate other sensors and interactive devices, such as audio sensors and gesture recognition, to achieve real-time response and feedback between lighting effects and user interaction, enhancing immersion and engagement.

[0125] In the above embodiments, multi-band decomposition and relative entropy calculation of audio data accurately capture the energy distribution relationship between different frequency bands, and the dominant frequency band is determined based on the topological structure of the energy flow graph, making subsequent beat detection more targeted. By constructing a phase tracking function within the dominant frequency band and combining it with hierarchical analysis of phase envelope features, the accuracy of beat marker location is improved. The recursive segmentation window and spectral centroid trajectory analysis method constructed based on beat markers can accurately extract emotional transition moments in the audio signal. Mapping the inflection point sequence to a polar coordinate system to establish a correspondence with LED light strip parameters achieves a natural transition from audio emotional features to visual effects. The spatial propagation mode designed based on the energy flow graph allows the visual effect of the LED light strip to remain synchronized with the energy flow characteristics of the audio, enhancing visual expressiveness. Through smoothing the visual parameters and constructing a gradient function, the color and brightness changes of the LED light strip can reflect the rhythm of the audio while maintaining the continuity of movement, avoiding abrupt changes and improving the smoothness of the overall visual experience. It captures the emotional progression and tension changes in music, and accurately controls the LED light strip to flash rhythmically according to the emotional progression and tension changes.

[0126] Before constructing the phase tracking function within the dominant frequency band in step S103 of the above embodiment, it is necessary to establish the correlation between the clock cycle and the phase tracking parameters to improve the targeting and accuracy of phase tracking. This will be discussed in conjunction with another embodiment below. Figure 2 Another method for controlling the rhythmic flashing of an LED strip in the embodiments of this application is described below:

[0127] Please see Figure 2 This is another flowchart illustrating a method for controlling the rhythmic flashing of an LED light strip in an embodiment of this application.

[0128] S201. Perform adaptive threshold segmentation on the energy distribution of the dominant frequency band and extract the energy peak points;

[0129] In this step, the system first needs to acquire the energy distribution data of the dominant frequency band of the audio signal. By performing time-frequency analysis on the audio signal, such as using Short-Time Fourier Transform (STFT), the energy distribution of the audio signal at different time and frequency points can be obtained. Then, the system performs adaptive thresholding segmentation on the energy distribution of the dominant frequency band. Adaptive thresholding segmentation is a commonly used image segmentation method. By dynamically adjusting the threshold, it can automatically segment the energy distribution map into different regions based on the characteristics of the energy distribution. For example, the system can find the optimal threshold using the maximum inter-class variance method to segment the energy distribution... Figure 2 The system performs value-based analysis to obtain the distribution of high and low energy regions. After obtaining the segmented energy distribution map, the system further analyzes the high-energy regions in the map to extract energy peak points. These energy peak points correspond to the time points with the highest energy in the audio signal, and typically also correspond to rhythmic points or accents in the music. The system can obtain the time locations of a series of energy peak points by finding local maxima in the energy distribution map. These energy peak points provide important information for subsequent beat cycle analysis.

[0130] S202. Calculate the time interval sequence between adjacent energy peak points;

[0131] After extracting the energy peaks in the dominant frequency band of the audio signal, the system needs to further analyze the temporal relationships between these peaks to obtain the periodicity information of the music rhythm. Therefore, in this step, the system calculates the time intervals between adjacent energy peaks, obtaining a time interval sequence. Specifically, the system can sort the time positions of the energy peaks and then calculate the time difference between adjacent peaks pair by pair to obtain the time interval sequence. For example, if the extracted energy peaks are t1, t2, t3, and t4 respectively, the time interval sequence can be represented as {t2-t1, t3-t2, t4-t3}. The time interval sequence reflects the interval distribution pattern of accented notes in the music rhythm, laying the foundation for subsequent beat period estimation. The system can also denoise the time interval sequence, removing outliers to improve the accuracy of subsequent analysis. By calculating the time interval sequence between peaks, the system can transform the rhythmic information of the audio signal into a discrete numerical sequence, providing data support for beat period analysis.

[0132] S203. Perform periodic analysis on the time interval sequence to determine the basic beat period;

[0133] In this step, the system needs to further analyze the time interval sequence obtained in the previous step to find its periodicity and determine the basic beat cycle of the music. Musical rhythms usually have obvious periodicity, meaning that the time intervals between accents repeat within a certain range. Therefore, by analyzing the periodicity of the time interval sequence, the beat cycle of the music can be estimated. The system can use various methods for periodic analysis, such as autocorrelation analysis and Fourier transform analysis. Autocorrelation analysis, by calculating the correlation between the signal and itself at different time delays, can discover repetition patterns in the signal. Performing autocorrelation analysis on the time interval sequence, the delay time corresponding to the peak point is a possible beat cycle. Fourier transform analysis, on the other hand, converts the time-domain signal into a frequency-domain signal, identifies prominent frequency components in the spectrum, and the corresponding period is the beat cycle. The system can comprehensively use multiple periodic analysis methods for mutual verification to improve the reliability of the beat cycle estimation. After obtaining possible candidate beat cycle values, the system can further optimize and adjust the beat cycle by combining information such as the stylistic characteristics of the music and user feedback, ultimately determining the basic beat cycle of the music. Determining the basic beat cycle provides a time reference for the rhythmic control of LED light strips.

[0134] S204. Set the time window length of the phase tracking function according to the basic beat cycle.

[0135] After determining the basic beat cycle of the music, the system needs to further set the time window length of the phase tracking function to achieve synchronized rhythm between the LED strip and the music. The phase tracking function obtains the precise time position of the music beat by analyzing the phase information of the audio signal. The setting of the time window length affects the analysis accuracy and real-time performance of the phase tracking function. If the time window is too long, it will lead to analysis lag, making it difficult to achieve real-time synchronization between the LED strip and the music; if the time window is too short, it will reduce the accuracy of the analysis and make it susceptible to noise interference. Therefore, the system needs to adaptively set the time window length of the phase tracking function according to the basic beat cycle. Generally, the time window length should be slightly larger than the basic beat cycle to ensure that the phase information of the audio signal can be completely analyzed within each beat cycle. For example, the system can set the time window length to 1.2 to 1.5 times the basic beat cycle. In addition, the system can also consider changes in music tempo and dynamically adjust the time window length. When the music tempo increases, the time window length is shortened accordingly; when the music tempo decreases, the time window length is lengthened accordingly. By adaptively setting the time window length of the phase tracking function, the system can always maintain the synchronized rhythm of the LED light strip with the music under different music rhythms, improving the flexibility and robustness of control.

[0136] In the above embodiments, adaptive threshold segmentation of the energy distribution in the dominant frequency band can automatically adjust the threshold according to signal characteristics, improving the robustness of energy peak point extraction. By analyzing the time interval sequence between adjacent energy peak points, a temporal relationship model of the beat is established. Periodic analysis of the time interval sequence accurately extracts the basic beat characteristics of the audio signal. The time window length of the phase tracking function is set according to the basic beat period, ensuring that the temporal resolution of the phase tracking matches the rhythmic characteristics of the audio signal. This scheme, through in-depth analysis of energy distribution characteristics, establishes a correlation between the beat period and phase tracking parameters, improving the targeting and accuracy of phase tracking, making subsequent beat marker location more precise and reliable.

[0137] The system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the physical device structure of a control system for rhythmic flashing of an LED light strip provided in an embodiment of this application.

[0138] It should be noted that, Figure 3 The structure of the system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0139] like Figure 3 As shown, the system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 302 or a program loaded from storage portion 308 into Random Access Memory (RAM) 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0140] The following components are connected to I / O interface 305: input section 306 including a camera, infrared sensor, etc.; output section 307 including a liquid crystal display (LCD) and speakers, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card and a modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0141] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0142] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0143] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0144] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or it may exist independently and not assembled into the system. The storage medium carries one or more computer programs that, when executed by a processor of a system, cause the system to implement the methods provided in the above embodiments.

[0145] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0146] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0147] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0148] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for controlling the rhythmic flashing of an LED light strip, characterized in that, include: The collected audio data is decomposed to obtain the energy distribution matrix of high frequency band, mid frequency band and low frequency band, and the relative entropy value between each frequency band is calculated. The relative entropy value includes high-mid entropy value, high-low entropy value and mid-low entropy value. An energy flow map is established based on the changing trend of the relative entropy value, and the dominant frequency band is determined according to the topological structure of the energy flow in the energy flow map. The energy flow map represents the direction and intensity of energy transfer between adjacent frequency bands. A phase tracking function is constructed within the dominant frequency band. The beat markers in the audio signal are located based on the phase tracking function, and the beat markers are classified into hierarchical levels according to the morphological characteristics of the phase envelope. A recursive segmentation window is constructed with the beat markers as boundaries. The spectral centroid trajectory within each recursive segmentation window is calculated, and the inflection point sequence of the spectral centroid trajectory is extracted. The inflection point sequence reflects the emotional turning point of the audio signal. The inflection point sequence is mapped to a polar coordinate system to establish the correspondence between polar coordinate angles and LED light strip color parameters, and the correspondence between polar diameter and brightness parameters. Based on the topology of the energy flow diagram, the spatial propagation mode of the LED light strip is designed so that the luminous efficacy along the propagation direction of the LED light strip is consistent with the energy flow direction between frequency bands, and the propagation speed is adjusted according to the energy flow intensity. The visual parameters between adjacent beat markers are smoothed to construct a gradient function that includes the inertial characteristics of the audio signal. The driving control sequence of the LED light strip is generated according to the gradient function, so that the LED light strip performs corresponding color and brightness changes according to the driving control sequence.

2. The method according to claim 1, characterized in that, The decomposition of the acquired audio data specifically includes: The audio data is subjected to overlapping frame processing, with the overlap rate between adjacent frames being a preset value, and a Hanning window is added to each frame of audio data. Wavelet packet decomposition is performed on each windowed audio data frame to obtain frequency subband coefficients; Based on the characteristics of human hearing, the frequency sub-band coefficients obtained by wavelet packet decomposition are reorganized into high-frequency, mid-frequency, and low-frequency bands, and the energy distribution matrix of each frequency band is calculated. The relative entropy values ​​between high and mid frequency bands, high and low frequency bands, and mid and low frequency bands are calculated based on the information entropy theory. The relative entropy values ​​are obtained by calculating the probability density function of the energy distribution of each frequency band.

3. The method according to claim 1, characterized in that, The step of establishing an energy flow map based on the changing trend of relative entropy specifically includes: Calculate the rate of change of the relative entropy values ​​of each frequency band between adjacent time frames, and construct a three-dimensional energy flow vector field; Topological analysis is performed on the three-dimensional energy flow vector field to identify energy convergence and divergence points; Based on the spatial distribution characteristics of the energy convergence points and the energy divergence points, an energy transfer network is constructed; The dominant frequency band is determined based on the connectivity analysis of the energy transfer network, and the dominant frequency band has the largest energy inflow-outflow ratio.

4. The method according to claim 1, characterized in that, Before constructing the phase tracking function within the dominant frequency band, the method further includes: Adaptive threshold segmentation is performed on the energy distribution of the dominant frequency band to extract energy peak points; Calculate the time interval sequence between adjacent energy peak points; Perform periodic analysis on the time interval sequence to determine the basic beat period; The time window length of the phase tracking function is set according to the basic beat cycle.

5. The method according to claim 1, characterized in that, The construction of the phase tracking function within the dominant frequency band specifically includes: Perform complex wavelet transform on the signal in the dominant frequency band to obtain phase information in the time-frequency plane; Calculate the time derivative of the phase information to obtain the instantaneous frequency curve; Detect abrupt changes in the instantaneous frequency curve and mark the locations of these abrupt changes as candidate beat points; The morphological features of the phase envelope are analyzed, and the candidate beat points are hierarchically divided according to the morphological features, including the steepness, width and symmetry of the phase envelope. A weight function is constructed based on the hierarchical information of the candidate beat points; The phase information is convolved with the weight function to obtain the enhanced phase features. A phase tracking function is constructed based on the enhanced phase features.

6. The method according to claim 5, characterized in that, Before constructing the recursive segmentation window using the beat markers as boundaries, the method further includes: Calculate the temporal distribution density of the candidate beat points at each level; A decision function for adaptive window size is constructed based on the aforementioned time distribution density; The depth of the recursive segmentation window is adjusted according to the decision function; Establish the hierarchical structure tree of the recursive segmentation window.

7. The method according to claim 5, characterized in that, The step of generating the driving control sequence for the LED light strip based on the gradient function specifically includes: Calculate the intermediate state parameters between adjacent candidate beat points based on the gradient function; The sequence of intermediate state parameters is spatially mapped according to the physical distribution of the LED strip to obtain the spatial mapping result; Calculate the propagation delay parameters based on the energy flow intensity; By integrating the spatial mapping results and the propagation delay parameters, a time-sequential drive control sequence is generated.

8. A control system for rhythmic flashing of an LED light strip, characterized in that, The system includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the system, the system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes computer program instructions that, when executed on a system, cause the system to perform the method as described in any one of claims 1-7.

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