LED lamp strip rhythm flickering control method and system, storage medium and program product

Abstract

The invention discloses an LED lamp strip rhythm flickering control method and system, a storage medium and a program product, and the method comprises the steps: calculating a relative entropy value among a high-frequency band, a middle-frequency band and a low-frequency band; establishing an energy flow diagram, and determining a dominant frequency band according to an energy flow topological structure in the energy flow diagram; a phase tracking function is constructed, and hierarchical level division is carried out on the beat mark points; establishing a recursive segmentation window, calculating a spectrum centroid trajectory, and extracting an inflection point sequence; establishing a corresponding relation between the polar coordinate angle and the color parameter of the LED lamp strip and a corresponding relation between the polar radius and the brightness parameter; a spatial propagation mode of the LED lamp strip is designed based on the topological structure of the energy flow diagram, so that the propagation direction of the light effect along the LED lamp strip is kept consistent with the energy flow direction between the frequency bands, and the propagation speed is adjusted according to the energy flow intensity; and generating a driving control sequence of the LED lamp strip according to the gradient function. The emotional progression and tension change of music are captured, and the LED lamp strip is accurately controlled to flicker according to the emotional progression and the tension change.

Description

Technical Field

[0001] The present 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 an LED light strip. Background Art

[0002] As people's pursuit of music rhythm effects continues to improve, the synchronous presentation of music rhythm and LED lights has become an important means to create atmosphere and enhance audio-visual experience. Traditional LED light strips can usually only perform simple switch control or preset flashing modes, and cannot be synchronized with the rhythm and emotional expression of music in real time, resulting in a single lighting effect and lack of interactivity, which is difficult to meet users' needs for immersive music experience.

[0003] At present, some audio control systems control the brightness and color changes of LED light strips by collecting the amplitude changes of audio signals, thus achieving basic synchronization between lights and music. This type of system uses Fourier transform to perform spectrum analysis on audio signals, and maps the energy distribution of different frequency bands to various parameters of LED light strips, so that the lighting effects can change accordingly with the rhythm of music.

[0004] However, when processing complex music, existing audio control systems often focus on the amplitude characteristics of audio signals, and the beat structure and emotional characteristics of the music are not obvious enough. As a result, although the rhythmic effect of the LED light strip can change with the music, it lacks a deep understanding of the inherent laws of music and it is difficult to accurately capture and express the emotional progression and tension changes of the music. Summary of the invention

[0005] The present application provides a control method, system, storage medium and program product for the rhythmic flashing of an LED light strip, which are 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, the present application provides a control method for the rhythmic flashing of an LED light strip, decomposing the collected audio data to obtain energy distribution matrices of high frequency bands, medium frequency bands and low frequency bands, and calculating the relative entropy values ​​between the frequency bands, the relative entropy values ​​including high and medium entropy values, high and low entropy values ​​and medium and low entropy values; An energy flow diagram 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 diagram. The energy flow diagram represents the direction and intensity of energy transfer between adjacent frequency bands. A phase tracking function is constructed in the dominant frequency band, beat markers in the audio signal are located according to the phase tracking function, and the beat markers are divided into hierarchical levels according to the morphological characteristics of the phase envelope; A recursive segmentation window is constructed with beat markers as demarcations, 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. Map the inflection point sequence to the polar coordinate system, and establish the corresponding relationship between the polar coordinate angle and the color parameter of the LED light strip and the corresponding relationship between the polar diameter and the brightness parameter; Design the spatial propagation mode of the LED light strip based on the topological structure of the energy flow diagram, so that the propagation direction of the light effect along 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; The visual parameters between adjacent beat marking points are smoothed, a gradient function containing the inertial characteristics of the audio signal is constructed, and a 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.

[0007] By adopting the above technical solutions, the audio data is decomposed into multiple frequency bands and the relative entropy value is calculated, the energy distribution relationship between different frequency bands is accurately captured, and the dominant frequency band is determined based on the topological structure of the energy flow diagram, so that the subsequent beat detection is more targeted. By constructing a phase tracking function in the dominant frequency band and combining the hierarchical analysis of the phase envelope characteristics, the accuracy of the beat marker point positioning is improved. The recursive segmentation window and spectral centroid trajectory analysis method based on the beat marker point can accurately extract the emotional turning point in the audio signal. The turning point sequence is mapped to the polar coordinate system to establish a corresponding relationship with the LED light strip parameters, realizing a natural transition from audio emotional features to visual effects. The spatial propagation mode designed based on the energy flow diagram enables the visual effects of the LED light strip to keep pace with the energy flow characteristics of the audio, enhancing the visual expression. By smoothing the visual parameters and constructing the gradient function, the color and brightness changes of the LED light strip can not only reflect the rhythm of the audio, but also maintain the continuity of the movement, avoiding abrupt mutations, and improving the fluency of the overall visual experience. It achieves the capture of the emotional progression and tension changes of the music, and accurately controls the rhythmic flashing of the LED light strip according to the emotional progression and tension changes.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, decomposing the collected audio data specifically includes: The audio data is processed by overlapping frames, the overlapping rate between adjacent frames is a preset value, and each frame of audio data is added with a Hanning window; Perform wavelet packet decomposition on each frame of windowed audio data to obtain frequency subband coefficients; According to the auditory characteristics of the human ear, the frequency sub-band coefficients obtained by wavelet packet decomposition are reorganized into high frequency band, medium frequency band and low frequency band, and the energy distribution matrix of each frequency band is calculated; Based on the information entropy theory, the relative entropy values ​​between high and medium frequency bands, high and low frequency bands, and medium and low frequency bands are calculated, where the relative entropy values ​​are calculated through the probability density function of the energy distribution of each frequency band.

[0009] By adopting the above technical solution and setting the processing mode of overlapping audio data frames, the continuity of information between adjacent frames is ensured, and energy leakage caused by signal mutation is avoided. Wavelet packet decomposition is performed on the audio data after windowing each frame to achieve fine division of the signal in the time and frequency domain. The frequency subband coefficients are reorganized according to the auditory characteristics of the human ear, so that the frequency band division is more in line with the laws of human auditory perception. The information entropy theory is used to calculate the relative entropy value, and the probability density function is used to characterize the similarity of energy distribution between frequency bands, providing a reliable mathematical basis for the extraction of energy flow characteristics. While ensuring the calculation efficiency, this scheme improves the rationality of frequency band division and the accuracy of energy feature extraction, making the subsequent beat detection and visual mapping more in line with human perception characteristics.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of establishing an energy flow diagram based on the change trend of the relative entropy value specifically includes: Calculate the rate of change of relative entropy values ​​of each frequency band between adjacent time frames and construct a three-dimensional energy flow vector field; Perform topological analysis on the three-dimensional energy flow vector field to identify energy convergence and divergence points; Construct an energy transfer network based on the spatial distribution characteristics of energy convergence points and divergence points; The dominant frequency band is determined based on the connectivity analysis of the energy transfer network, which has the largest energy inflow and outflow ratio.

[0011] By adopting the above technical solution, the rate of change of the relative entropy value of each frequency band between adjacent time frames is calculated, and a three-dimensional vector field that can characterize the dynamic change of energy is constructed. The vector field is topologically analyzed to identify the energy convergence points and divergence points, revealing the key nodes of energy transfer in the audio signal. The energy transfer network constructed based on the spatial distribution characteristics reflects the overall characteristics of the energy flow between frequency bands. The dominant frequency band is determined by the connectivity analysis of the energy transfer network, ensuring that the selected dominant frequency band has the most significant energy transfer characteristics. This analysis method based on the energy topological structure makes the selection of the dominant frequency band more objective and reliable, provides a more stable feature basis for subsequent beat detection, and improves the system's adaptability to different types of audio signals.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, before constructing the phase tracking function in the dominant frequency band, the method further includes: Perform adaptive threshold segmentation on the energy distribution of the dominant frequency band and extract the energy peak point; Calculate the time interval sequence between adjacent energy peak points; Conduct periodic analysis on the time interval sequence to determine the basic beat period; Set the time window length of the phase tracking function according to the basic beat period.

[0013] By adopting the above technical solution, the energy distribution of the dominant frequency band is adaptively threshold segmented, and the threshold can be automatically adjusted according to the signal characteristics, thereby improving the robustness of the energy peak point extraction. By analyzing the time interval sequence between adjacent energy peak points, a temporal relationship model of the beat is established. The time interval sequence is periodically analyzed to accurately extract the basic beat characteristics of the audio signal. The time window length of the phase tracking function is set according to the basic beat cycle, so that the time resolution of the phase tracking matches the rhythm characteristics of the audio signal. Through in-depth analysis of the energy distribution characteristics, this solution establishes a correlation between the beat cycle and the phase tracking parameters, improves the pertinence and accuracy of phase tracking, and makes the subsequent beat marker point positioning more accurate and reliable.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, constructing a phase tracking function within a dominant frequency band specifically includes: Perform complex wavelet transform on the signal of the dominant frequency band to obtain the phase information on the time-frequency plane; Calculate the time derivative of the phase information to obtain the instantaneous frequency curve; Detect the mutation point of the instantaneous frequency curve and mark the position of the mutation point as a candidate beat point; Analyze the morphological features of the phase envelope and classify the candidate beat points according to the morphological features, including the steepness, width and symmetry of the phase envelope; Construct a weight function based on the hierarchical information of candidate beat points; The phase information is convolved with the weight function to obtain the enhanced phase feature; A phase tracking function is constructed based on the enhanced phase features.

[0015] By adopting the above technical solution, the dominant frequency band signal is subjected to complex wavelet transform, and the complete phase information on the time-frequency plane is obtained, retaining the time-frequency characteristics of the signal. The instantaneous frequency curve is obtained by calculating the time derivative of the phase information, which can reflect the dynamic change characteristics of the signal frequency. By detecting the mutation points of the instantaneous frequency curve as candidate beat points, the hierarchical division is carried out in combination with the morphological characteristics such as the steepness, width and symmetry of the phase envelope, and the accurate quantification of the strength of the beat is achieved. The weight function is constructed based on the hierarchical information of the candidate beat points, and the convolution operation is performed with the phase information to enhance the phase characteristics of the real beat points, while suppressing the influence of noise and false beat points. The phase tracking function finally constructed comprehensively considers the time-frequency characteristics, beat level and phase enhancement effect of the signal, improves the accuracy and robustness of beat detection, and enables the system to adapt to changes in different music styles and rhythm characteristics.

[0016] In combination with some embodiments of the first aspect, in some embodiments, before constructing the recursive segmentation window with the beat marker points as demarcations, the method further includes: Calculate the time distribution density of beat points at each level; Constructing a decision function for adaptive window size based on time distribution density; Adjust the depth of recursive segmentation according to the decision function; Create a hierarchical structure tree for recursive partitioning.

[0017] By adopting the above technical solution, the time distribution density of beat points at each level is calculated, and the rhythm change law of the audio signal in the time dimension is mastered. A decision function of adaptive window size is constructed based on the time distribution density, so that the size of the recursive segmentation window can be automatically adjusted according to the time characteristics of the beat. The depth of recursive segmentation is dynamically adjusted through the decision function to avoid the problem of over-segmentation or under-segmentation. The established recursive segmentation hierarchical structure tree clearly expresses the hierarchical time structure of the audio signal, so that the system can automatically select the appropriate analysis granularity according to the complexity of the music, improving the accuracy and computational efficiency of the spectral centroid trajectory analysis. This adaptive recursive segmentation scheme enhances the system's ability to analyze the time structure of the audio signal, making the extraction of emotional turning points more accurate and reliable.

[0018] In combination with some embodiments of the first aspect, in some embodiments, generating a driving control sequence of the LED light strip according to the gradient function specifically includes: Calculate the intermediate state parameters between adjacent beat points based on the gradient function; The sequence of the intermediate state parameters is spatially mapped according to the physical distribution of the LED light strip to obtain a spatial mapping result; Calculate the propagation delay parameters based on the energy flow intensity; Integrate the spatial mapping results and propagation delay parameters to generate a timed drive control sequence.

[0019] By adopting the above technical solution, the intermediate state parameters between adjacent beat points are calculated based on the gradient function, so as to achieve a smooth transition of the visual effect of the LED light strip. The intermediate state parameter sequence 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 parameters are calculated according to the energy flow intensity, so that the propagation speed of the light effect can truly reflect the energy change characteristics of the audio signal. By integrating the spatial mapping results and the propagation delay parameters to generate a timed drive control sequence, the unified control of the LED light strip in the time and space dimensions is achieved, so that the visual effect can accurately express the rhythm of the audio while maintaining the continuity and naturalness of the movement, thereby enhancing the artistic expression of the overall visual effect.

[0020] In the second aspect, an embodiment of the present application provides a control system for the rhythmic flashing of an LED light strip, and the control system for the rhythmic flashing of an LED light strip comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a control method for the rhythmic flashing of an LED light strip, which performs multi-band decomposition and relative entropy value calculation on audio data, accurately captures the energy distribution relationship between different frequency bands, and determines the dominant frequency band based on the topological structure of the energy flow diagram, so that the subsequent beat detection is more targeted. By constructing a phase tracking function in the dominant frequency band and combining the hierarchical analysis of the phase envelope characteristics, the accuracy of the beat marker point positioning is improved. The recursive segmentation window and spectral centroid trajectory analysis method based on the beat marker point can accurately extract the emotional turning point in the audio signal. The inflection point sequence is mapped to the polar coordinate system to establish a corresponding relationship with the LED light strip parameters, realizing a natural transition from audio emotional features to visual effects. The spatial propagation mode designed based on the energy flow diagram enables the visual effects of the LED light strip to be synchronized with the energy flow characteristics of the audio, thereby enhancing the visual expression. By smoothing the visual parameters and constructing the gradient function, the color and brightness changes of the LED light strip can not only reflect the rhythm of the audio, but also maintain the continuity of the movement, avoid abrupt mutations, and improve the fluency of the overall visual experience. It achieves the capture of the emotional progression and tension changes of the music, and accurately controls the rhythmic flashing of the LED light strip according to the emotional progression and tension changes.

[0024] 2. The present application provides a control method for the rhythmic flashing of an LED light strip, which performs adaptive threshold segmentation on the energy distribution of the dominant frequency band, can automatically adjust the threshold according to the signal characteristics, and improves the robustness of the energy peak point extraction. By analyzing the time interval sequence between adjacent energy peak points, a temporal relationship model of the beat is established. The time interval sequence is periodically analyzed to accurately extract the basic beat characteristics of the audio signal. The time window length of the phase tracking function is set according to the basic beat cycle, so that the time resolution of the phase tracking matches the rhythm characteristics of the audio signal. Through an in-depth analysis of the energy distribution characteristics, this scheme establishes a correlation between the beat cycle and the phase tracking parameters, improves the pertinence and accuracy of the phase tracking, and makes the subsequent beat marker point positioning more accurate and reliable.

[0025] 3. The present application provides a control method for the rhythmic flashing of an LED light strip, calculates the time distribution density of beat points at each level, and grasps the rhythm change law of the audio signal in the time dimension. A decision function for adaptive window size is constructed based on the time distribution density, so that the size of the recursive segmentation window can be automatically adjusted according to the time characteristics of the beat. The depth of the recursive segmentation is dynamically adjusted through the decision function to avoid the problem of over-segmentation or under-segmentation. The established recursive segmentation hierarchical structure tree clearly expresses the hierarchical time structure of the audio signal, so that the system can automatically select the appropriate analysis granularity according to the complexity of the music, thereby improving the accuracy and computational efficiency of the spectral centroid trajectory analysis. This adaptive recursive segmentation scheme enhances the system's ability to analyze the time structure of the audio signal, making the extraction of emotional turning points more accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a method for controlling the rhythmic flashing of an LED light strip in an embodiment of the present application.

[0027] Figure 2 This is another flow chart of a method for controlling the rhythmic flashing of an LED light strip in an embodiment of the present application.

[0028] Figure 3 It 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 the present application. DETAILED DESCRIPTION

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0030] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0031] Combine the following Figure 1 , a method for controlling the rhythmic flashing of an LED light strip in an embodiment of the present application is described: See also Figure 1, which is a flow chart of a method for controlling the rhythmic flashing of an LED light strip in an embodiment of the present application.

[0032] S101, decomposing the collected audio data to obtain energy distribution matrices of high frequency band, medium frequency band and low frequency band, and calculating relative entropy values ​​between each frequency band; The system decomposes the collected audio data to obtain the energy distribution matrix of the high frequency band, the medium frequency band and the low frequency band. Specifically: the audio data is overlapped and framed, the overlap rate between adjacent frames is a preset value, and each frame of audio data is added with a Hanning window; Perform wavelet packet decomposition on each frame of windowed audio data to obtain frequency subband coefficients; According to the auditory characteristics of the human ear, the frequency sub-band coefficients obtained by wavelet packet decomposition are reorganized into high frequency band, medium frequency band and low frequency band, and the energy distribution matrix of each frequency band is calculated; Based on the information entropy theory, the relative entropy values ​​between high and medium frequency bands, high and low frequency bands, and medium and low frequency bands are calculated, where the relative entropy values ​​are calculated through the probability density function of the energy distribution of each frequency band.

[0033] Calculate the relative entropy value between each frequency band, the relative entropy value includes high and medium entropy value, high and low entropy value and medium and low entropy value.

[0034] The main purpose of this step is to perform frequency domain decomposition of audio data, obtain the energy distribution characteristics of different frequency bands, and calculate the relative entropy value between frequency bands for subsequent analysis of the frequency domain structure and energy flow law of audio signals. The system first preprocesses the collected audio data, such as removing DC components and normalizing, to improve the quality and stability of the signal. Then, the system segments the audio data in an overlapping frame manner, and adds a Hanning window to each frame of data to reduce the spectrum leakage effect. Next, the system performs wavelet packet decomposition on each frame of windowed audio data, divides the signal into multiple frequency subbands, and calculates the frequency coefficients of each subband. According to the auditory characteristics of the human ear, the system reorganizes the frequency subbands obtained by wavelet packet decomposition into high frequency bands, medium frequency bands and low frequency bands, and calculates the energy distribution matrix of each frequency band. Finally, the system calculates the relative entropy values ​​between high and medium frequency bands, high and low frequency bands, and medium and low frequency bands based on the information entropy theory, where the relative entropy values ​​are calculated by the probability density function of the energy distribution of each frequency band.

[0035] In the frequency domain decomposition process, the system can use different time-frequency analysis methods, such as short-time Fourier transform (STFT), wavelet transform (WT), etc., and select appropriate decomposition strategies according to the characteristics of the audio signal. For wavelet packet decomposition, the system can select different wavelet basis functions, such as Daubechies wavelet, Symlets wavelet, etc., to optimize the 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. At the same time, the system can design an adaptive frequency band division strategy to dynamically adjust the boundaries of the high, medium and low frequency bands according to the spectral structure of the audio signal, thereby improving the flexibility and adaptability of frequency band division.

[0036] In addition, the system can also introduce a time smoothing mechanism when calculating the relative entropy value, and perform sliding average or weighted average on the relative entropy value sequence to suppress the influence of noise and instantaneous fluctuations. For complex audio scenes, such as multi-channel, stereo, etc., the system can perform frequency domain decomposition and relative entropy value calculation for each channel separately, and comprehensively analyze the relationship between channels and energy distribution differences to obtain more comprehensive and accurate frequency domain features. In short, by performing frequency domain decomposition and relative entropy value calculation on audio data, the system can deeply analyze the frequency domain structure and energy flow law of audio signals, and provide important feature information for subsequent rhythm analysis, visual mapping and other steps.

[0037] S102, establishing an energy flow diagram based on the change trend of the relative entropy value, and determining a dominant frequency band according to the topological structure of energy flow in the energy flow diagram; The system establishes an energy flow diagram based on the changing trend of the relative entropy value. Specifically, it calculates the change rate of the relative entropy value of each frequency band between adjacent time frames and constructs a three-dimensional energy flow vector field. Perform topological analysis on the three-dimensional energy flow vector field to identify energy convergence and divergence points; Construct an energy transfer network based on the spatial distribution characteristics of energy convergence points and divergence points; The dominant frequency band is determined based on the connectivity analysis of the energy transfer network, which has the largest energy inflow and outflow ratio.

[0038] The dominant frequency band is determined based on the topological structure of energy flow in the energy flow diagram, which characterizes the direction and intensity of energy transfer between adjacent frequency bands.

[0039] The main purpose of this step is to establish an energy flow diagram based on the changing trend of the relative entropy value, characterize the energy transfer and interaction rules between different frequency bands of the audio signal, and further analyze the topological structure of the energy flow diagram to determine the frequency band that plays a dominant role in the entire audio clip. The system first calculates the rate of change of the relative entropy value of each frequency band between adjacent time frames to obtain a three-dimensional vector field that reflects the direction and intensity of energy flow between frequency bands. Then, the system performs a topological analysis on the three-dimensional energy flow vector field, identifies the energy convergence points (sources) and divergence points (sinks), and characterizes the inflow and outflow characteristics of energy between frequency bands. Next, the system constructs an energy transfer network between frequency bands based on the spatial distribution characteristics of the energy convergence points and divergence points, revealing the energy flow topological structure of the audio signal in the frequency domain. Finally, the system calculates the energy inflow and outflow ratio of each frequency band by performing a connectivity analysis on the energy transfer network, and determines the frequency band with the largest energy inflow and outflow ratio as the dominant frequency band.

[0040] When constructing the energy flow diagram, the system can use different mathematical models and calculation methods, such as vector field topological analysis, graph theory algorithms, etc., to accurately and efficiently characterize the energy transfer law between frequency bands. For complex audio signals, the system can introduce a multi-scale analysis strategy to construct energy flow diagrams at different time scales to capture the energy flow characteristics of audio signals at different time resolutions. At the same time, the system can design an adaptive topological structure recognition algorithm to dynamically adjust the judgment threshold of energy convergence points and divergence points according to the distribution characteristics of the energy flow vector field, thereby improving the robustness and adaptability of topological structure analysis.

[0041] In addition, the system can also comprehensively consider other musical features, such as rhythm and melody, when determining the dominant frequency band, so as to more comprehensively evaluate the importance and dominance of the frequency band in the entire audio clip. For musical works with multiple themes or paragraphs, the system can adopt a segmented analysis strategy to independently construct an energy flow diagram and determine the dominant frequency band in each paragraph, and obtain the distribution law of the dominant frequency band of the entire song through comparison and integration between paragraphs. In short, by constructing an energy flow diagram and analyzing its topological structure, the system can deeply explore the laws of energy transfer and interaction of audio signals in the frequency domain, providing important decision-making basis for applications such as music rhythm analysis and emotional expression, and has important theoretical and practical value in the fields of music information retrieval and audio analysis.

[0042] S103, constructing a phase tracking function in the dominant frequency band, locating beat markers in the audio signal according to the phase tracking function, and dividing the beat markers into hierarchical levels according to the morphological characteristics of the phase envelope; The system constructs a phase tracking function in the dominant frequency band. Specifically: a complex wavelet transform is performed on the signal in the dominant frequency band to obtain the phase information on the time-frequency plane; Calculate the time derivative of the phase information to obtain the instantaneous frequency curve; Detect the mutation point of the instantaneous frequency curve and mark the position of the mutation point as a candidate beat point; Analyze the morphological features of the phase envelope and classify the candidate beat points according to the morphological features, including the steepness, width and symmetry of the phase envelope; Construct a weight function based on the hierarchical information of candidate beat points; The phase information is convolved with the weight function to obtain the enhanced phase feature; A phase tracking function is constructed based on the enhanced phase features.

[0043] The beat markers in the audio signal are located according to the phase tracking function, and the beat markers are divided into hierarchical levels according to the morphological characteristics of the phase envelope.

[0044] The main purpose of this step is to construct a phase tracking function in the dominant frequency band, and to achieve accurate positioning and hierarchical division of beat markers by analyzing the phase information of the audio signal, so as to provide 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 on the time-frequency plane. Then, the system calculates the derivative of the phase information with respect to time to obtain an instantaneous frequency curve reflecting the frequency change trend. By detecting the mutation points of the instantaneous frequency curve, the system can find the candidate beat points in the audio signal. Next, the system analyzes the morphological features of the phase envelope near the candidate beat points, such as steepness, width, and symmetry, and divides the candidate beat points into hierarchical levels based on this, and obtains the marking information reflecting the beat structure hierarchy. Finally, the system constructs a weight function based on the hierarchical level information of the beat marker points, convolves the phase information with the weight function, obtains an enhanced phase feature, and constructs a phase tracking function based on the enhanced phase feature to achieve accurate positioning and tracking of the beat marker points.

[0045] When constructing the phase tracking function, the system can use different time-frequency analysis tools, such as Hilbert-Huang transform, synchronous compression transform, etc., to extract the instantaneous phase information of the audio signal. For complex music signals, the system can introduce multi-scale and multi-resolution phase analysis strategies to improve the accuracy and robustness of beat marker positioning by extracting and fusing phase information at different time and frequency scales. At the same time, the system can design an adaptive phase envelope morphological feature extraction and classification algorithm, dynamically adjust the judgment threshold and classification strategy of the morphological feature according to prior knowledge such as music style and rhythm type, and realize the intelligent and personalized hierarchical level division of beat markers.

[0046] In addition, the system can also use music theory knowledge and rules, such as the law of beat strength and the structure of the measure, to correct and optimize the hierarchical level division results of the beat marker points, so as to improve the musical rationality and accuracy of the division. When tracking the beat marker points, the system can adopt an adaptive time window strategy, dynamically adjust the analysis window size of the phase tracking function according to the density and change law of the music rhythm, so as to balance the time resolution and frequency resolution, and realize the accurate positioning and real-time tracking of the beat marker points. In short, by constructing a phase tracking function in the dominant frequency band and dividing the beat marker points into hierarchical levels in combination with the phase envelope morphological characteristics, the system can accurately characterize the rhythm structure and hierarchy of the music signal, and provide key rhythm marking information for applications such as music beat synchronization and dance choreography, which has broad application prospects.

[0047] S104, constructing a recursive segmentation window with beat markers as demarcations, calculating the spectrum centroid trajectory within each recursive segmentation window, and extracting the inflection point sequence of the spectrum centroid trajectory; Prior to this step, the system calculates the temporal distribution density of beat points at each level; Constructing a decision function for adaptive window size based on time distribution density; Adjust the depth of recursive segmentation according to the decision function; Create a hierarchical structure tree for recursive partitioning.

[0048] Then, a recursive segmentation window is constructed with beat markers as demarcations, the spectral centroid trajectory within each recursive segmentation window is calculated, and the inflection point sequence of the spectral centroid trajectory is extracted.

[0049] The main purpose of this step is to construct a recursive segmentation window with beat markers as the boundary, and further refine and quantify the rhythm structure and change law of the music signal by calculating the spectral centroid trajectory in each segmentation window and extracting the inflection point sequence. Before this step, the system first calculates the time distribution density of beat points at each level, and constructs a decision function of adaptive window size based on the time distribution density to determine the optimal time scale and resolution of 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 level and time relationship. Next, the system uses the beat markers as the boundary, constructs the corresponding time window at each node of the recursive segmentation tree, and performs spectral analysis on the audio signal in each time window to calculate the centroid trajectory of the energy distribution in the window. By smoothing the spectral centroid trajectory and detecting inflection points, the system can obtain an inflection point sequence that reflects the trend of rhythm changes, and characterize the temporal dynamic characteristics of the music rhythm at different segmentation levels.

[0050] When constructing the recursive segmentation window, the system can adopt a variety of time segmentation strategies, such as equal interval segmentation, dynamic time warping (DTW), etc., to adapt to the characteristics of different music styles and rhythm types. For the calculation of the spectral centroid trajectory, the system can choose different spectrum analysis methods, such as Fourier transform, constant Q transform, etc., to balance the time resolution and frequency resolution. When extracting the inflection point sequence, the system can design an adaptive inflection point detection algorithm, and dynamically adjust the threshold and window size of the inflection point judgment according to the smoothness, change rate and other characteristics of the spectral centroid trajectory to accurately capture the key moments of rhythm changes. At the same time, the system can also introduce multi-scale and multi-resolution inflection point analysis strategies to comprehensively characterize the multi-level structure and change law of music rhythm by extracting and fusing inflection point information at different time scales.

[0051] In addition, the system can use music theory knowledge and empirical rules to interpret and annotate the extracted inflection point sequence with musical semantics, such as identifying key rhythmic events such as time signature changes and syncopation changes. When applied to LED light strip control, the system can map the inflection point sequence with the physical structure and layout of the LED light strip, and generate the spatiotemporal coordinates and parameter sequence of the lighting effect based on the time position, amplitude and other attributes of the inflection point, so as to achieve precise synchronization and dynamic mapping of music rhythm and lighting effects. In short, by constructing a recursive segmentation window with beat markers as the boundary and extracting the inflection point sequence of the spectral centroid trajectory, the system can analyze the temporal dynamic characteristics of music rhythm at multiple time scales and resolutions, and provide rich rhythm control information for applications such as music visualization and stage lighting design, which has high practical value and innovation.

[0052] S105, mapping the inflection point sequence to a polar coordinate system, establishing a corresponding relationship between the polar coordinate angle and the color parameter of the LED light strip and a corresponding relationship between the polar diameter and the brightness parameter; The main purpose of this step is to map the extracted inflection point sequence into the polar coordinate system, and establish the correspondence between the polar coordinate parameters and the visual parameters of the LED light strip, so as to realize the conversion and mapping of the music rhythm characteristics to the lighting effect parameters. The system first selects a suitable polar coordinate system, such as a circle, a spiral, etc., according to the physical layout and control requirements of the LED light strip. Then, the system maps the inflection point sequence into the polar coordinate system in chronological order, and extracts the polar coordinate angle and polar diameter value corresponding to each inflection point. Next, the system establishes the correspondence between the polar coordinate angle and the color parameter of the LED light strip, and the correspondence between the polar diameter and the brightness parameter. For the color parameter, the system can divide the polar coordinate angle into several intervals and map each interval to a specific color value or color change mode. For the brightness parameter, the system can linearly or nonlinearly map the polar diameter value to the brightness value to realize continuous modulation and dynamic change of the brightness. Finally, the system outputs the mapped color and brightness parameter sequence as an instruction sequence for controlling the visual effect of the LED light strip.

[0053] When performing polar coordinate mapping of the inflection point sequence, the system can use different mapping functions and interpolation algorithms, such as linear interpolation, spline interpolation, etc., to achieve a smooth transition and continuous mapping between the inflection point position and the polar coordinate parameters. For the mapping of color parameters, the system can use color theory and aesthetic principles to design reasonable color schemes and change rules, such as complementary color matching, gradient colors, etc., to enhance the artistic expression and emotional rendering of lighting effects. At the same time, the system can introduce user interaction and feedback mechanisms to allow users to customize or adjust color mapping rules to meet personalized visual preferences and creative needs.

[0054] In addition, the system can also optimize and enhance the polar coordinate mapping results in combination with other music features and contextual information. For example, according to the emotional characteristics and style type of the music, the saturation, contrast and other parameters of the color mapping can be dynamically adjusted to match the emotional tone and performance style of the music. When controlling the LED light strip, the system can comprehensively consider the physical characteristics, visual effects, power consumption limitations and other factors of the light strip, and perform appropriate scaling, limiting and other processing on the color and brightness parameters to ensure the stability, consistency and reliability of the lighting effects. In short, by mapping the inflection point sequence into the polar coordinate system and establishing a corresponding relationship between the polar coordinate parameters and the visual parameters of the LED light strip, the system can realize an intuitive and vivid mapping and transformation of the music rhythm characteristics into the lighting effects, providing a novel and flexible form of expression and interaction for applications such as music visualization and light art, greatly expanding the application scenarios and creative space of LED light strips.

[0055] S106. Designing a spatial propagation mode of the LED light strip based on the topological structure of the energy flow diagram, so that the propagation direction of the light effect along the LED light strip is consistent with the energy flow direction between frequency bands, and adjusting the propagation speed according to the energy flow intensity; The main purpose of this step is to design the spatial propagation mode of the LED light strip based on the topological structure of the energy flow diagram, and to achieve the dynamic association and expression of the light effect and the energy distribution of the music by synchronizing and mapping the light effect propagation with the energy flow between frequency bands. The system first analyzes the topological structure of the energy flow diagram and extracts key information reflecting the direction and intensity of energy transfer between frequency bands, such as energy flow direction, flow velocity, and convergence point. Then, according to the physical layout and topological structure of the LED light strip, the system designs a light effect propagation mode that matches the energy flow diagram, so that the propagation direction of the light effect along the LED light strip is consistent with the energy flow direction between frequency bands. Specifically, the system can divide the LED light strip into several logical areas or control units, and determine the light effect propagation order and priority of each area according to the energy flow direction. At the same time, the system can dynamically adjust the speed and gradient effect of the light effect propagation according to the energy flow intensity, so that the dynamic change of the light effect propagation matches the time evolution of the music energy distribution. Finally, the system generates a spatiotemporal coordinate sequence and parameter sequence for controlling the light effect propagation of the LED light strip, and realizes the physical output and display of the light effect through the hardware interface and drive circuit.

[0056] When designing the spatial propagation mode of LED light strips, the system can adopt different topological structures and mapping strategies, such as linear topology, ring topology, grid topology, etc., 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, shortest path, etc., to achieve automatic planning and optimization of light effect propagation paths. At the same time, the system can design multi-level propagation modes and hierarchical control strategies to achieve coordination and synchronization of light effect propagation in different spatial ranges and time granularities according to the hierarchical structure and time scale of energy flow.

[0057] S107, calculating the deviation value between the theoretical concentration change curve and the concentration data actually collected of the target pollutant; The system first extracts the visual parameter sequence between adjacent beat markers, such as color, brightness, position, etc. Then, the system smoothes these visual parameter sequences, eliminates mutations and discontinuities, and generates a smooth curve or gradient function. When constructing the gradient function, the system considers the inertial characteristics of the audio signal, such as the continuity of the rhythm, the gradual change of the speed, etc., and introduces inertial terms and smooth terms between adjacent beats to match the changes in visual parameters with the flow characteristics of the music rhythm. Next, the system calculates the intermediate state parameters of the LED light strip between adjacent beats based on the gradient function, such as the intermediate color value, brightness value, etc. 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 in combination with the energy flow intensity, generating a complete LED light strip drive control sequence to achieve smooth evolution and dynamic presentation of the lighting effect.

[0058] When performing visual parameter smoothing, the system can use different interpolation algorithms and smoothing strategies, such as linear interpolation, spline interpolation, Gaussian smoothing, etc., and select the optimal smoothing method and parameter settings according to the characteristics of the music rhythm and the requirements of the lighting effects. For the gradient function that contains the inertial characteristics of the audio signal, the system can introduce relevant concepts of physics and signal processing, such as moment of inertia, attenuation coefficient, convolution kernel, etc., and achieve dynamic synchronization and natural transition between visual parameter changes and music rhythm by reasonably designing the mathematical models of inertia terms and smoothing terms. At the same time, the system can use adaptive parameter estimation and optimization technology to adjust the parameters of the gradient function in real time according to the changing characteristics of the music rhythm and user feedback to achieve the best visual effect and user experience.

[0059] In addition, the system can also combine other music elements and visual elements to artistically and creatively design and customize the gradient function. For example, according to the emotional characteristics of the music, the meaning of the lyrics, etc., design matching color gradient schemes and animation effects to enhance the expressiveness and appeal of the lighting effects. When controlling the LED light strip, the system can make full use of the performance and characteristics of the hardware equipment, such as using high-precision PWM control and time synchronization mechanisms to ensure the accuracy, stability and real-time performance of the lighting effects. In short, by smoothing the visual parameters between adjacent beat markers and constructing a gradient function containing the inertial characteristics of the audio signal, the system can achieve a smooth transition and natural synchronization between the visual effects of the LED light strip and the rhythm of the music, greatly enhancing the artistic expression and interactive experience of the lighting effects, and providing a novel, flexible and creative technical means and implementation method for applications such as music visualization and lighting art.

[0060] S108, performing smoothing processing on visual parameters between adjacent beat marking points, constructing a gradient function including inertial features of the audio signal, and generating a driving control sequence of the LED light strip according to the gradient function.

[0061] The system smoothes the visual parameters between adjacent beat markers, constructs a gradient function that includes the inertial characteristics of the audio signal, and generates a drive control sequence for the LED light strip based on the gradient function, so that the LED light strip performs corresponding color and brightness changes according to the drive control sequence. The drive control sequence for the LED light strip generated based on the gradient function specifically includes: Calculate the intermediate state parameters between adjacent beat points based on the gradient function; The sequence of the intermediate state parameters is spatially mapped according to the physical distribution of the LED light strip to obtain a spatial mapping result; Calculate the propagation delay parameters based on the energy flow intensity; Integrate the spatial mapping results and propagation delay parameters to generate a timed drive control sequence.

[0062] The main purpose of this step is to finely control and synchronously output the visual parameters, and achieve high consistency and synchronization between the LED light strip light effect and the music beat by optimizing the gradient function and feedback mechanism, creating an immersive experience that is highly integrated with the music emotion and atmosphere. According to the generated LED light strip drive control sequence, the system accurately controls the color, brightness and other visual parameters of each lamp bead in time and space to ensure that the changes in the lighting effects are perfectly synchronized with the flow of the music rhythm. At the same time, the system uses high-precision clocks and synchronization mechanisms, such as timestamps and frame synchronization, to ensure the coordination and consistency between the lamp beads and avoid visual breaks and jumps. During the output control process, the system monitors the synchronization and consistency between the music beat and the lighting effects in real time, and dynamically adjusts the parameters of the gradient function, such as the inertia coefficient and smoothing factor, through the feedback mechanism, to make the lighting effect more natural and smooth, and perfectly match the music emotion and rhythm. In addition, the system can also introduce intelligent algorithms and machine learning technologies to adaptively optimize the gradient function and control strategy by analyzing user feedback and interaction data, and continuously improve the artistic expression and user experience of the lighting effects.

[0063] When finely controlling visual parameters, the system can make full use of the physical characteristics and performance advantages of LED light strips, such as high dynamic range, wide color gamut, low power consumption, etc., and achieve colorful and delicate changes in lighting effects through reasonable color management and brightness modulation. For large-scale LED light strip arrays, the system can adopt distributed control architecture and parallel processing technology to achieve collaborative work and load balancing between multiple control units to ensure real-time and stability of control. At the same time, the system can also integrate other sensors and interactive devices, such as audio sensors, gesture recognition, etc., to achieve real-time response and feedback of lighting effects and user interaction, and enhance immersion and participation.

[0064] In the above embodiment, the audio data is decomposed into multiple frequency bands and the relative entropy value is calculated, the energy distribution relationship between different frequency bands is accurately captured, and the dominant frequency band is determined based on the topological structure of the energy flow diagram, so that the subsequent beat detection is more targeted. By constructing a phase tracking function in the dominant frequency band and combining the hierarchical analysis of the phase envelope characteristics, the accuracy of the beat marker point positioning is improved. The recursive segmentation window and spectral centroid trajectory analysis method based on the beat marker point can accurately extract the emotional turning point in the audio signal. The inflection point sequence is mapped to the polar coordinate system to establish a corresponding relationship with the LED light strip parameters, realizing the natural transition from audio emotional features to visual effects. The spatial propagation mode designed based on the energy flow diagram enables the visual effects of the LED light strip to keep pace with the energy flow characteristics of the audio, enhancing the visual expression. Through the smoothing of the visual parameters and the construction of the gradient function, the color and brightness changes of the LED light strip can not only reflect the rhythm of the audio, but also maintain the continuity of the movement, avoid abrupt mutations, and improve the fluency of the overall visual experience. It achieves the capture of the emotional progression and tension changes of the music, and accurately controls the rhythmic flashing of the LED light strip according to the emotional progression and tension changes.

[0065] Before constructing the phase tracking function in the dominant frequency band in step S103 of the above embodiment, it is also necessary to establish a correlation between the beat cycle and the phase tracking parameter to improve the pertinence and accuracy of the phase tracking. Figure 2 , another method for controlling the rhythmic flashing of an LED light strip in an embodiment of the present application is described: See also Figure 2 , is another flow chart of a method for controlling the rhythmic flashing of an LED light strip in an embodiment of the present application.

[0066] S201, performing adaptive threshold segmentation on the energy distribution of the dominant frequency band to extract energy peak points; In this step, the system first needs to obtain 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 methods such as short-time Fourier transform (STFT), the energy distribution of the audio signal at different time points and frequency points can be obtained. Then, the system performs adaptive threshold segmentation on the energy distribution of the dominant frequency band. Adaptive threshold segmentation is a commonly used image segmentation method. By dynamically adjusting the threshold, the energy distribution map can be automatically segmented into different areas according to the characteristics of the energy distribution. For example, the system can find the optimal threshold through the maximum inter-class variance method and divide the energy distribution into different areas. Figure 2After obtaining the segmented energy distribution graph, the system further analyzes the high-energy areas in the graph and extracts the energy peak points. The energy peak points correspond to the time points with the highest energy in the audio signal, and usually also correspond to the rhythm points or accent points of the music. The system can obtain the time positions of a series of energy peak points by obtaining the local maximum points in the energy distribution graph. These energy peak points provide important information for the subsequent beat cycle analysis.

[0067] S202, calculating the time interval sequence between adjacent energy peak points; After extracting the energy peak points in the dominant frequency band of the audio signal, the system needs to further analyze the time relationship between these peak points to obtain the periodic information of the music rhythm. Therefore, in this step, the system calculates the time intervals between adjacent energy peak points to obtain a time interval sequence. Specifically, the system can sort the time positions of the energy peak points, and then calculate the time difference of adjacent peak points one by one to obtain the time interval sequence. For example, if the time positions of the extracted energy peak points are t1, t2, t3, and t4, the time interval sequence can be expressed as {t2-t1, t3-t2, t4-t3}. The time interval sequence reflects the interval distribution law of the accent points in the music rhythm, laying the foundation for the subsequent beat cycle estimation. The system can also perform denoising on the time interval sequence to remove some outliers to improve the accuracy of subsequent analysis. By calculating the time interval sequence between peak points, the system can convert the rhythm information of the audio signal into a discrete numerical sequence, providing data support for the analysis of the beat cycle.

[0068] S203, performing periodic analysis on the time interval sequence to determine the basic beat period; In this step, the system needs to further analyze the time interval sequence obtained in the previous step to find out the periodicity law in order to determine the basic beat cycle of the music. Music rhythm usually has obvious periodicity, that is, the time interval between the accent points repeats 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 a variety of methods for periodic analysis, such as autocorrelation analysis, Fourier transform analysis, etc. Autocorrelation analysis can find repetitive patterns in the signal by calculating the correlation between the signal and itself at different time delays. Autocorrelation analysis is performed on the time interval sequence, and the delay time corresponding to the peak point is the possible beat cycle. Fourier transform analysis converts the time domain signal into a frequency domain signal to find the prominent frequency components in the spectrum, and the corresponding period is the beat cycle. The system can comprehensively adopt a variety of periodic analysis methods and verify each other to improve the reliability of the beat cycle estimation. After obtaining the possible candidate values ​​of the beat cycle, the system can also further optimize and adjust the beat cycle in combination with the style characteristics of the music, user feedback and other information, and finally determine the basic beat cycle of the music. The determination of the basic beat cycle provides a time reference for the rhythmic control of the LED light strip.

[0069] S204, setting the time window length of the phase tracking function according to the basic beat cycle.

[0070] 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 the synchronous rhythm of the LED light strip and the music rhythm. 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, the analysis will be delayed, making it difficult to achieve real-time synchronization between the light strip and the music; if the time window is too short, the accuracy of the analysis will be reduced and it will be 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 fully 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 the change in music speed and dynamically adjust the time window length. When the music speed increases, the time window length is shortened accordingly; when the music speed slows down, the time window length is extended accordingly. By adaptively setting the time window length of the phase tracking function, the system can always keep the LED light strip synchronized with the music at different music rhythms, thereby improving the flexibility and robustness of the control.

[0071] In the above embodiment, the energy distribution of the dominant frequency band is adaptively threshold segmented, and the threshold can be automatically adjusted according to the signal characteristics, thereby improving the robustness of the energy peak point extraction. By analyzing the time interval sequence between adjacent energy peak points, a temporal relationship model of the beat is established. The time interval sequence is periodically analyzed to accurately extract the basic beat characteristics of the audio signal. The time window length of the phase tracking function is set according to the basic beat cycle, so that the time resolution of the phase tracking matches the rhythm characteristics of the audio signal. Through an in-depth analysis of the energy distribution characteristics, this solution establishes a correlation between the beat cycle and the phase tracking parameters, improves the pertinence and accuracy of the phase tracking, and makes the subsequent beat marker point positioning more accurate and reliable.

[0072] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which 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 the present application.

[0073] It should be noted that Figure 3 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0074] like Figure 3 As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0075] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0076] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0077] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0078] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0079] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0080] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0081] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0082] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part 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, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0083] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for controlling the rhythmic flashing of an LED light strip, characterized in that: include: Decomposing the collected audio data to obtain energy distribution matrices of high frequency band, medium frequency band and low frequency band, and calculating relative entropy values ​​between each frequency band, wherein the relative entropy values ​​include high and medium entropy values, high and low entropy values ​​and medium and low entropy values; An energy flow diagram is established based on the variation trend of the relative entropy value, and a dominant frequency band is determined according to the topological structure of energy flow in the energy flow diagram, wherein the energy flow diagram represents the direction and intensity of energy transfer between adjacent frequency bands; Constructing a phase tracking function in the dominant frequency band, locating beat markers in the audio signal according to the phase tracking function, and dividing the beat markers into hierarchical levels according to the morphological characteristics of the phase envelope; Constructing a recursive segmentation window with the beat marker points as demarcations, calculating the spectral centroid trajectory within each of the recursive segmentation windows, and extracting an inflection point sequence of the spectral centroid trajectory, wherein the inflection point sequence reflects the emotional turning point of the audio signal; Mapping the inflection point sequence into a polar coordinate system, establishing a corresponding relationship between the polar coordinate angle and the color parameter of the LED light strip and a corresponding relationship between the polar diameter and the brightness parameter; Designing the spatial propagation mode of the LED light strip based on the topological structure of the energy flow diagram, so that the propagation direction of the light effect along the LED light strip is consistent with the energy flow direction between frequency bands, and adjusting the propagation speed according to the energy flow intensity; The visual parameters between adjacent beat marking points are smoothed, a gradient function including the inertial characteristics of the audio signal is constructed, and a drive control sequence of the LED light strip is generated according to the gradient function, so that the LED light strip performs corresponding color changes and brightness changes according to the drive control sequence.

2. The method according to claim 1, characterized in that Decomposing the collected audio data specifically includes: The audio data is subjected to overlapping frame processing, the overlapping rate between adjacent frames is a preset value, and each frame of audio data is subjected to a Hanning window; Perform wavelet packet decomposition on each frame of windowed audio data to obtain frequency subband coefficients; Reorganize the frequency subband coefficients obtained by decomposing the wavelet packet into a high frequency band, a mid-frequency band and a low frequency band according to the auditory characteristics of the human ear, and calculate the energy distribution matrix of each frequency band; The relative entropy values ​​between high and medium frequency bands, high and low frequency bands, and medium and low frequency bands are calculated based on the information entropy theory, wherein the relative entropy values ​​are calculated by 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 diagram based on the change trend of the relative entropy value specifically includes: Calculating the change rate of the relative entropy value of each frequency band between adjacent time frames to construct a three-dimensional energy flow vector field; Performing topological analysis on the three-dimensional energy flow vector field to identify energy convergence points and divergence points; constructing an energy transfer network according to the spatial distribution characteristics of the energy convergence points and the divergence points; A dominant frequency band is determined based on connectivity analysis of the energy transfer network, wherein the dominant frequency band has a maximum energy inflow and outflow ratio.

4. The method according to claim 1, characterized in that: Before constructing the phase tracking function in the dominant frequency band, the method further includes: Performing adaptive threshold segmentation on the energy distribution of the dominant frequency band to extract energy peak points; Calculating a time interval sequence between adjacent energy peak points; Performing periodic analysis on the time interval sequence to determine a 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 constructing of a phase tracking function within the dominant frequency band specifically includes: Performing complex wavelet transform on the signal of the dominant frequency band to obtain phase information on the time-frequency plane; Calculating the time derivative of the phase information to obtain an instantaneous frequency curve; Detecting a mutation point of the instantaneous frequency curve, and marking the position of the mutation point as a candidate beat point; Analyzing the morphological features of the phase envelope, and hierarchically classifying the candidate beat points according to the morphological features, wherein the morphological features include the steepness, width, and symmetry of the phase envelope; Constructing a weight function according to the hierarchical information of the candidate beat points; Performing a convolution operation on the phase information and the weight function to obtain an enhanced phase feature; A phase tracking function is constructed based on the enhanced phase feature.

6. The method according to claim 1, characterized in that Before constructing the recursive segmentation window with the beat marking points as demarcations, the method further includes: Calculate the time distribution density of the beat points at each level; Constructing a decision function for adaptive window size based on the time distribution density; adjusting the depth of the recursive segmentation according to the decision function; A hierarchical structure tree of the recursive partitioning is established.

7. The method according to claim 1, characterized in that Generating the driving control sequence of the LED light strip according to the gradient function specifically includes: Calculate the intermediate state parameters between adjacent beat points based on the gradient function; Performing spatial mapping on the sequence of the intermediate state parameters according to the physical distribution of the LED light strip to obtain a spatial mapping result; Calculating a propagation delay parameter according to the energy flow intensity; The spatial mapping result and the propagation delay parameter are integrated to generate a time-sequential drive control sequence.

8. A control system for rhythmic flashing of LED light strips, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute 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 a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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