Intelligent adjustment system for landscape lighting based on tone and motion state fusion analysis

Abstract

This invention discloses an intelligent landscape lighting control system based on the fusion analysis of tone and motion states, belonging to the field of digital information processing technology. It addresses the problems of logical conflicts in multimodal data and step abrupt changes in control signals. First, frequency domain transformation and differentiation are performed on the collected spatiotemporal sequence data to generate normalized tone and motion intensity. Then, the cooperative gain coefficient and dispersion index are calculated to quantify the scene activity level and data deviation. Next, based on the dispersion, a reliability weight is calculated, and a fusion iterative operation is performed on preset parameters and historical states to generate target control parameters. Finally, a smooth-transition pulse-width modulation digital sequence is generated using a linear interpolation algorithm, and control commands are output. This system achieves high-reliability fusion of multi-source data and adaptive smoothing of light effect control.

Description

Technical Field

[0001] This invention relates to the field of digital information processing technology, specifically to a landscape lighting intelligent adjustment system based on the fusion analysis of tone and motion state. Background Technology

[0002] With the diversification of urban public space functions, landscape lighting has evolved from simple nighttime functional lighting to creating spatial atmosphere and enhancing interactive experiences. In open areas such as plazas, parks, and commercial pedestrian streets, lighting systems have become an important medium connecting visitors with the environment. They can adjust the light environment in real time according to the activity status of people, which is of great significance for enhancing the vitality of public spaces and improving the visual experience of visitors.

[0003] However, existing landscape lighting adjustment methods suffer from substantial technical flaws in practical applications. On one hand, most existing control logics rely on single-modal data triggering, such as determining light brightness solely based on the amplitude of sound signals or switching lighting modes based solely on the trigger frequency of infrared sensors. This approach fails to perform deep semantic analysis of the scene and is easily affected by ambient background noise. This can lead to false triggering of active modes when high-decibel non-human noise is detected, or misjudging a scene as deserted when people are talking, resulting in a mismatch between the lighting environment and the actual psychological state of the people. On the other hand, existing systems often employ threshold-based transition mechanisms in control signal generation, lacking verification and smoothing of multi-dimensional data consistency. This causes abrupt brightness jumps during mode switching, affecting not only visual comfort but also reducing system control stability. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a landscape lighting intelligent adjustment system based on the fusion analysis of tone and motion state, thus solving the problems mentioned above.

[0005] To achieve the above objectives, this invention provides the following technical solution: a landscape lighting intelligent adjustment system based on the fusion analysis of tone and motion states, comprising the following modules: a feature extraction and quantization module, used to perform short-time Fourier transform on the acquired audio frame sequence, extract the fundamental frequency mean and frequency fluctuation variance in the frequency domain as tone feature components, and perform normalization processing to generate tone intensity values; simultaneously, it performs differential operations on the acquired moving target coordinate sequence, extracts the displacement velocity mean and direction change rate per unit time as motion feature components, and performs normalization processing to generate motion intensity values; and a collaborative interaction calculation module, used to perform linear weighted summation of tone intensity values ​​and motion intensity values ​​to obtain the interactive collaborative gain coefficient characterizing sound-motion synchronization, and determine the current scene's... The system comprises a base activity level module, which calculates the ratio deviation between tone intensity and motion intensity values ​​to generate a data dispersion index; a confidence-based luminous efficacy correction module, which retrieves preset initial lighting parameters using the base activity level as an index, substitutes the data dispersion index into an inverse proportional weighting function to calculate the confidence weight of the current data, performs weighted fusion iterative calculations on the initial lighting parameters and historical lighting parameters, and generates target color temperature and target brightness values; and a timing interpolation driving module, which converts the target color temperature and target brightness values ​​into target pulse width modulation (PWM) values, constructs an execution queue containing the current PWM value and the target PWM value, generates a transition step sequence between the current and target values ​​through a linear interpolation algorithm, and outputs PWM control commands sequentially according to a preset clock cycle.

[0006] Furthermore, the specific process of performing a short-time Fourier transform on the acquired audio frame sequence, extracting the fundamental frequency mean and frequency fluctuation variance in the frequency domain as tone feature components, and performing normalization processing to generate tone intensity values ​​is as follows: The audio frame sequence is windowed by segmentation using the Hanning window function, and a fast Fourier transform is performed to obtain the spectral amplitude spectrum of each frame. The fundamental frequency peak point is located using a spectral peak search algorithm, and the average value of the fundamental frequency peak points in multiple consecutive frames is analyzed as the fundamental frequency mean. At the same time, the second-order central moment of the fundamental frequency peak point on the time axis is quantized as the frequency fluctuation variance. The fundamental frequency mean and frequency fluctuation variance are projected onto a preset emotional feature mapping interval, respectively. The projected feature data is compressed into the interval between zero and one using the Sigmoid activation function, and the compressed fundamental frequency data and variance data are merged through weighted aggregation operation to output the tone intensity value representing the emotional activity of the voice.

[0007] Furthermore, the specific process of performing differential operations on the collected moving target coordinate sequence, extracting the mean displacement velocity and direction change rate per unit time as motion feature components, and performing normalization processing to generate motion intensity values ​​is as follows: First-order differential operations are performed on adjacent coordinate points in the moving target coordinate sequence to analyze the Euclidean distance and obtain the instantaneous velocity vector magnitude. The arithmetic mean of all instantaneous velocity vector magnitudes within a preset time window is calculated as the mean displacement velocity. Simultaneously, the geometric angle relationship between adjacent instantaneous velocity vectors is extracted, and the tangential angle change of the motion trajectory is analytically obtained through the inverse cosine function. The cumulative value of the angle change per unit time is used as the direction change rate. The mean displacement velocity and direction change rate are mapped to a standard dimensionless interval through max-min standardization. The standardized mean velocity is used as the active component, and the standardized direction change rate is used as the entropy component. The active component and entropy component are weighted and fused to output a motion intensity value representing the intensity of group behavior.

[0008] Furthermore, the specific process of determining the basic activity level of the current scene by linearly weighting and summing the tone intensity value and motion intensity value to obtain the interactive cooperative gain coefficient representing the sound-motion synchronization is as follows: A preset environmental bias weight coefficient is retrieved, and priority weights are assigned to the tone intensity value and motion intensity value respectively. The weighted sum of the two is analyzed as the initial cooperative modulus. The tone intensity value and motion intensity value are coupled and modulated to generate a coupling gain term. The initial cooperative modulus and the coupling gain term are superimposed to generate the interactive cooperative gain coefficient. A multi-level stepped threshold decision logic is constructed, and the interactive cooperative gain coefficient is compared sequentially with the preset quiet threshold, interactive threshold, and carnival threshold. When the coefficient falls into different threshold ranges, the corresponding discretized state identifier is output as the basic activity level of the current scene.

[0009] Furthermore, the specific process of simultaneously calculating the ratio deviation between tone intensity value and motion intensity value to generate the data dispersion index is as follows: Analyze the difference modulus between tone intensity value and motion intensity value as the modal absolute error, and calculate the arithmetic sum of tone intensity value and motion intensity value as the modal total intensity; perform a superposition operation on the modal total intensity and regularization constant to generate a denominator term, and calculate the numerical relationship between the modal absolute error and the denominator term through relative ratio quantization to generate a relative deviation ratio; input the relative deviation ratio into a preset exponential amplification function, perform nonlinear gain amplification on the input deviation signal, and output the amplified value as the data dispersion index.

[0010] Furthermore, the specific process of retrieving preset initial lighting parameters using the basic activity level as an index and substituting the data dispersion index into the inverse weighting function to calculate the credibility weight of the current data is as follows: An associated database containing the activity level field and the lighting parameter field is established. The basic activity level is used as the key value for matching queries in the database, extracting the corresponding color temperature and brightness values ​​as initial lighting parameters. An inverse weighting calculation logic is constructed, setting a preset sensitivity coefficient and a baseline constant. The sensitivity coefficient and the data dispersion index are multiplied and modulated to generate an attenuation term. The baseline constant and the attenuation term are superimposed to generate a denominator. The credibility weight of the current data is output through the mapping between the baseline constant and the reciprocal of the denominator.

[0011] Furthermore, the specific process of generating the target color temperature value and target brightness value by performing weighted fusion iterative calculation on the initial lighting parameters and historical lighting parameters is as follows: The output value of the previous control cycle is read from the system register as the historical lighting parameter. The inertia retention weight is obtained by complementary calculation of the unit value and the confidence weight. The initial lighting parameters and the confidence weight are weighted and modulated to generate a new state component. The historical lighting parameters and the inertia retention weight are weighted and modulated to generate a historical state component. Vector aggregation is performed on the new state component and the historical state component, and the fused color temperature data and brightness data are output as the target color temperature value and target brightness value, respectively.

[0012] Furthermore, the specific process of converting the target color temperature value and target brightness value into target pulse width modulation (PWM) values ​​and constructing an execution queue containing the current PWM value and the target PWM value is as follows: The preset color temperature mixing ratio algorithm is invoked, and the power allocation ratio of the warm color temperature channel and the cool color temperature channel is calculated based on the target color temperature value. The target duty cycle of each channel is mapped to the target brightness value, and then quantized into the target PWM value. The PWM value currently being output in the register at the current time is read as the current node, and the target PWM value is used as the termination node. A doubly linked list containing the start timestamp, the current node, and the termination node is constructed as the execution queue.

[0013] Furthermore, the specific process of generating a transition step sequence between the current and target values ​​using a linear interpolation algorithm and outputting pulse width modulation control instructions sequentially according to a preset clock cycle is as follows: Calculate the numerical difference between the termination node and the current node, obtain the preset total transition duration, and derive the single-step change slope by analyzing the ratio of the numerical difference to the number of clock cycles contained in the total transition duration; initialize the accumulator, and when the rising edge of each preset clock cycle arrives, perform cumulative iteration on the output value of the previous cycle and the single-step change slope to generate the intermediate step value at the current moment; convert the intermediate step value into a binary control signal, write it into the driver register through a general-purpose input / output interface, and output the pulse width modulation control instruction.

[0014] The present invention has the following beneficial effects:

[0015] (1) The intelligent landscape lighting adjustment system based on the fusion analysis of tone and motion states achieves accurate quantitative identification of complex scene atmospheres through the cooperation of the feature extraction quantification module and the collaborative interaction calculation module. The system first performs frequency domain transformation and differential operation on audio and motion data to extract the tone intensity that reflects emotional color and the motion intensity that reflects intense behavior. Then, it uses linear weighted summation and ratio deviation calculation to establish an interactive collaborative gain coefficient that represents the synchronization of sound and motion from a mathematical perspective. The advantage of this data processing mechanism is that it no longer relies solely on the absolute intensity of the signal, but judges the authenticity of the scene by calculating the dispersion index between tone and motion. When the tone is high but the motion is still, or the motion is intense but the tone is low, the system can identify the high dispersion of the data and determine it as an atypical active scene, thereby effectively shielding the single-dimensional environmental interference, solving the misjudgment problem caused by the lack of multimodal verification in the existing technology, and greatly improving the accuracy of scene recognition.

[0016] (2) The intelligent landscape lighting control system based on the fusion analysis of tone and motion states achieves adaptive stability and visual smoothness of lighting control through the collaboration of the confidence-based luminous efficacy correction module and the temporal interpolation driving module. The system uses the data dispersion index to construct an inverse proportional weighting function, dynamically calculates the confidence weight of the current data, and introduces historical lighting parameters for weighted fusion iterative calculation. This means that when there are data conflicts or instability, the system will automatically reduce the weight of the current data and increase the inertia of the historical state, avoiding flickering of the light due to sensor data jitter. At the same time, the temporal interpolation driving module constructs an execution queue and generates a transition ladder sequence, converting the target luminous efficacy parameters into continuously changing pulse width modulation instructions, ensuring the linear transition of the lighting equipment in the process of color temperature and brightness adjustment, solving the visual abruptness caused by the step of the control signal in the prior art, and improving the comfort perceived by the human eye.

[0017] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0018] Figure 1 This is a flowchart of the intelligent landscape lighting adjustment system based on the fusion analysis of tone and motion state according to the present invention. Detailed Implementation

[0019] This application's embodiment solves the technical problems of existing landscape lighting systems being easily affected by interference from a single environmental signal, leading to misjudgment, and the abrupt switching process of light effects, through a landscape lighting intelligent adjustment system based on the fusion analysis of tone and motion state.

[0020] The overall approach of the scheme in this application embodiment is as follows: First, the system uses digital signal processing technology to perform feature dimensionality reduction on the collected raw audio and motion trajectory data, extracting normalized tone intensity and motion intensity; then, in the data fusion layer, it calculates the collaborative gain and ratio deviation of the two to quantify the activity level of the scene and the credibility of the data; subsequently, based on the data credibility, it performs weighted iteration on preset lighting parameters and historical states to generate target control parameters that take into account both sensitivity and stability; finally, it uses a linear interpolation algorithm to map the target parameters into a continuous pulse width modulation timing signal, driving the lighting equipment to perform smooth light effect adjustment, thereby achieving precise matching between the light environment and the behavior and emotions of the crowd.

[0021] Please see Figure 1 This invention provides a technical solution: a landscape lighting intelligent adjustment system based on the fusion analysis of tone and motion states, comprising the following modules: a feature extraction and quantization module, used to perform short-time Fourier transform on the acquired audio frame sequence, extract the fundamental frequency mean and frequency fluctuation variance in the frequency domain as tone feature components, and perform normalization processing to generate tone intensity values; simultaneously, it performs differential operation on the acquired moving target coordinate sequence, extracts the displacement velocity mean and direction change rate per unit time as motion feature components, and performs normalization processing to generate motion intensity values; and a collaborative interaction calculation module, used to perform linear weighted summation of tone intensity values ​​and motion intensity values ​​to obtain the interactive collaborative gain coefficient characterizing sound-motion synchronization, and determine the basic activity of the current scene. The system employs a multi-level approach, simultaneously calculating the ratio deviation between tone intensity and motion intensity values ​​to generate a data dispersion index. A confidence-based luminous efficacy correction module retrieves preset initial lighting parameters using the basic activity level as an index, substitutes the data dispersion index into an inverse weighting function to calculate the confidence weight of the current data, and performs weighted fusion iterative calculations on the initial and historical lighting parameters to generate target color temperature and target brightness values. A timing interpolation-driven module converts the target color temperature and target brightness values ​​into target pulse width modulation (PWM) values, constructs an execution queue containing the current and target PWM values, generates a transition ladder sequence between the current and target values ​​using a linear interpolation algorithm, and outputs PWM control commands sequentially according to a preset clock cycle.

[0022] In this implementation scheme, the feature extraction and quantization module is primarily responsible for converting analog signals from the physical world into standardized digital feature vectors that can be processed by a computer. This module first performs a short-time Fourier transform on the acquired audio frame sequence. This is a mathematical transformation method that maps time-domain signals to the frequency domain, used to analyze the frequency composition of the sound signal within different time windows, thereby accurately extracting the fundamental frequency mean and frequency fluctuation variance representing the emotional tone of the sound. Simultaneously, the module performs differential operations on the coordinate sequence of the moving target, i.e., calculating the derivative of the coordinate points over time, to obtain the instantaneous displacement velocity and rate of change of direction of the target. To eliminate the difference in physical dimensions between audio frequency and motion velocity, the module uses a normalization algorithm to map the above feature components to a dimensionless interval of zero to one, outputting tone intensity and motion intensity values. The technical role of this module is to provide a unified standard of data input for subsequent data fusion, solving the technical challenge of directly performing mathematical operations on multimodal heterogeneous data. The collaborative interaction computing module is the core computing unit of the system, used to evaluate the activity level of the current scene and the logical consistency of the data. The module performs a linear weighted summation operation on the input tone intensity and motion intensity values, synthesizing an interactive cooperative gain coefficient representing sound-motion synchronization through preset weighting coefficients. This coefficient directly reflects the overall energy level of the scene and is used to determine the basic activity level of the current scene. Simultaneously, the module calculates the ratio deviation between the two to generate a data dispersion index, a statistical indicator used to quantify the degree of deviation between sound and motion features. For example, in a scene with loud noise but no human movement, this index will significantly increase. The technical function of this module is to mathematically distinguish between effective active scenes and ineffective noise interference scenes, ensuring the system's semantic understanding capability in complex environments. The confidence-based lighting effect correction module is mainly used to generate final lighting control parameters that balance sensitivity and stability. The module first uses the basic activity level as an index key to retrieve the corresponding initial lighting parameters from a preset database, i.e., the color temperature and brightness that theoretically best match the current activity level. Subsequently, the module substitutes the data dispersion index into an inverse proportional weighting function, a non-linear mathematical model whose logic is that the higher the dispersion, the lower the calculated weight value, thereby reducing the confidence in the current conflicting data. The module utilizes calculated confidence weights to perform weighted fusion and iterative calculations on the current initial lighting parameters and the historical lighting parameters from the previous moment, essentially using the inertia of historical data to smooth fluctuations in the current data. The module's technical function lies in constructing an adaptive filtering mechanism based on data confidence, effectively preventing severe flickering of the lighting state caused by sudden environmental noise or sensor jitter. The timing interpolation driver module is primarily responsible for converting the target parameters at the logic level into timing control signals executable by the underlying hardware. The module first quantizes the target color temperature and target brightness values ​​into target pulse width modulation (PWM) values. PWM is a technique that controls the power of analog circuits by adjusting the duty cycle of digital signals.Next, the module constructs an execution queue containing the current and target values, and applies a linear interpolation algorithm to calculate a series of intermediate step values ​​between the current and target values ​​according to a preset clock cycle, generating a smoothly transitioning step sequence. The technical function of this module is to solve the step problem of digital control signals during state transitions, achieving continuous and smooth changes in lighting effects over time, avoiding visually perceptible abrupt changes.

[0023] Specifically, the process of performing a short-time Fourier transform on the acquired audio frame sequence, extracting the fundamental frequency mean and frequency fluctuation variance in the frequency domain as tone feature components, and then normalizing them to generate tone intensity values ​​is as follows: The audio frame sequence is segmented and windowed using the Hanning window function, and a fast Fourier transform is performed to obtain the spectral amplitude spectrum of each frame. The fundamental frequency peak point is located using a spectral peak search algorithm, and the average value of the fundamental frequency peak points in multiple consecutive frames is analyzed as the fundamental frequency mean. At the same time, the second-order central moment of the fundamental frequency peak point on the time axis is quantized as the frequency fluctuation variance. The fundamental frequency mean and frequency fluctuation variance are projected onto a preset emotional feature mapping interval, respectively. The projected feature data is compressed into the interval between zero and one using the Sigmoid activation function, and the compressed fundamental frequency data and variance data are merged through a weighted aggregation operation to output the tone intensity value representing the emotional activity of the voice.

[0024] In this implementation scheme, the audio data processing first involves preprocessing and frequency domain transformation. Since the original audio signal is non-stationary in the time domain, direct truncation would cause spectral leakage. Therefore, the Hanning window function is used to perform frame-by-frame windowing on the audio frame sequence. The smoothing properties of the window function reduce abrupt changes at signal truncation points and suppress sidelobe interference. Subsequently, a Fast Fourier Transform is performed to convert the discrete-time signal into a frequency domain signal, obtaining the amplitude spectrum of each frame. This step transforms the invisible sound waveform into frequency distribution data that can be recognized by a computer. Next, a peak search algorithm is used to locate the frequency point with the highest energy in the amplitude spectrum as the fundamental frequency peak point, which represents the main pitch of the human voice. To quantify the emotional characteristics of the sound, the system calculates the fundamental frequency mean and frequency fluctuation variance. The fundamental frequency mean reflects the overall pitch level of the sound, and the calculation formula is as follows: ;in, : Fundamental frequency mean; The total number of audio frames within a statistical time window; The index of the current audio frame; :No. The frame is calculated using a peak search algorithm to extract the fundamental frequency peak value. Meanwhile, the frequency fluctuation variance characterizes the degree of vibration in the sound, i.e., the stability of emotion. High variance typically corresponds to emotions like excitement or panic. Its calculation is based on the principle of the second-order central moment, as shown in the following formula: ;in, : Frequency fluctuation variance (standard deviation is used here to unify the dimension series); , , and The definition is consistent with the aforementioned formula. Finally, in order to transform the above physical characteristics into control signals that the system can process, normalization mapping is required. The system uses the Sigmoid activation function to compress the feature data to the interval between zero and one. This function has good nonlinear saturation characteristics and can suppress the influence of extreme outliers. Subsequently, the fundamental frequency and variance data are combined through weighted aggregation operations to generate the tone intensity value, calculated as follows: ;in, The final output is the normalized pitch intensity value; The preset weighting coefficient for the fundamental frequency characteristic is determined by analyzing the correlation between pitch and activity in historical scene data; for example, a value of 0.6 is used. :Preset baseband reference center value; : The preset variance reference center value; The base of the natural logarithm. Through the above calculations, the system successfully transforms abstract vocal emotions into calculable numerical indicators.

[0025] Specifically, the process of performing differential operations on the collected coordinate sequence of the moving target, extracting the mean displacement velocity and the rate of change of direction per unit time as motion feature components, and then normalizing them to generate a motion intensity value is as follows: First-order differential operations are performed on adjacent coordinate points in the coordinate sequence of the moving target to analyze the Euclidean distance and obtain the magnitude of the instantaneous velocity vector. The arithmetic mean of the magnitudes of all instantaneous velocity vectors within a preset time window is calculated as the mean displacement velocity. Simultaneously, the geometric angle relationship between adjacent instantaneous velocity vectors is extracted, and the change in tangential angle of the motion trajectory is obtained analytically through the inverse cosine function. The cumulative value of the angle change per unit time is used as the rate of change of direction. The mean displacement velocity and the rate of change of direction are mapped to a standard dimensionless interval through max-min standardization. The standardized mean velocity is used as the active component, and the standardized rate of change of direction is used as the entropy component. The active component and the entropy component are weighted and fused to output a motion intensity value representing the intensity of group behavior.

[0026] In this implementation scheme, the core of the motion data processing lies in analyzing the dynamic characteristics of the target. First, first-order differential operations are performed on adjacent coordinate points in the target's coordinate sequence, i.e., instantaneous velocity is obtained by calculating the change in position over time. During this process, the magnitude of the instantaneous velocity vector is analyzed using the Euclidean distance formula, a standard mathematical method for calculating the straight-line distance between two points in a two-dimensional plane. The arithmetic mean within a preset time window is then used as the average displacement velocity, which directly reflects the movement rate of the crowd. Next, to capture the degree of disorder in group behavior (i.e., the entropy component), the system extracts the geometric angle relationship between adjacent instantaneous velocity vectors. The cosine value is calculated by the ratio of the vector dot product to the product of the magnitudes, and then the inverse cosine function is used to analyze the change in the tangential angle of the trajectory. The accumulated angle change per unit time is used as the rate of change of direction. This step aims to distinguish between two different high-energy-consuming states: straight-line running and disorderly play. To generate the final motion intensity value, the system uses a minimax normalization method to map the above features with different physical dimensions to a standard dimensionless interval and performs weighted fusion. This calculation process considers not only the magnitude of the velocity but also the complexity of the direction. The specific calculation formula is as follows: ;in, The final output is the normalized motion intensity value; Weighting coefficient for the mean displacement velocity; The weighting coefficient of the rate of change of direction, and The weights are determined based on the scenario's pre-defined preferences for speed sensitivity and order sensitivity; The calculated average displacement velocity; Preset minimum speed threshold; Preset maximum speed threshold; The index of the time sampling point; T: the total number of times within the sampling window; At that moment The instantaneous velocity vector; At that moment The instantaneous velocity vector; Represents the modulo operation of vectors; Represents the dot product operation of vectors; : The preset upper limit constant for the normalization of the rate of change of direction. Using this formula, the system can comprehensively determine whether the target is moving rapidly in a straight line or in violent, disorderly motion, thus outputting an accurate indicator of the intensity of group behavior.

[0027] Specifically, the process of linearly weighting and summing the tone intensity value and motion intensity value to obtain the interactive collaborative gain coefficient representing the sound-motion synchronization and determining the basic activity level of the current scene is as follows: A preset environmental bias weight coefficient is retrieved, and priority weights are assigned to the tone intensity value and motion intensity value respectively. The weighted sum of the two is analyzed as the initial collaborative modulus. The tone intensity value and motion intensity value are coupled and modulated to generate a coupling gain term. The initial collaborative modulus and the coupling gain term are superimposed to generate the interactive collaborative gain coefficient. A multi-level stepped threshold decision logic is constructed, and the interactive collaborative gain coefficient is compared sequentially with preset quiet threshold, interactive threshold, and carnival threshold. When the coefficient falls into different threshold ranges, the corresponding discretized state identifier is output as the basic activity level of the current scene.

[0028] In this implementation scheme, the core of the collaborative interaction computing module's processing lies in fusing multimodal data using mathematical methods to assess the true activity level of the scene. First, the system retrieves a preset environmental bias weighting coefficient. This coefficient is a parameter pre-set based on the physical attributes of the application scene; for example, higher weight is given to tone in a music plaza scene, while higher weight is given to motion in a sports park scene. After assigning priority weights to tone intensity and motion intensity values ​​respectively, the weighted sum of the two is analyzed as the initial collaborative modulus, representing the basic energy at the linear superposition level. To further capture the characteristics of audio-visual synchronization, the system introduces a coupling modulation mechanism, which multiplies the tone intensity and motion intensity values ​​to generate a coupling gain term. This step utilizes the non-linear amplification characteristic of multiplication; the coupling term only significantly increases when both sound and motion are at high values, thus rewarding synchronized and active behavior. The initial collaborative modulus is superimposed with the coupling gain term to generate the final interactive collaborative gain coefficient, calculated as follows: ;in, Interactive collaboration gain coefficient, used to quantify the overall active energy of the current scene; : Environmental bias weighting coefficient for tone intensity value; The environmental bias weighting coefficient of the exercise intensity value, and The tone intensity value generated in the previous steps; : The motion intensity value generated in the previous steps; The preset synchronization gain factor is used to adjust the reward level for acoustic synchronization phenomena, typically ranging from 0.3 to 0.5. After obtaining continuous gain coefficients, the system constructs a multi-level stepped threshold decision logic to discretize them. The interactive coordination gain coefficients are sequentially compared with preset quiet thresholds, interactive thresholds, and carnival thresholds. This processing method is similar to the quantization process in digital signal processing, mapping continuously changing values ​​to easily processed computer state labels (such as state 0, state 1, state 2). When the coefficients fall into different threshold ranges, the corresponding discretized state labels are output as the basic activity level of the current scene, thus providing a definite index basis for subsequent table lookup operations.

[0029] Specifically, the process of simultaneously calculating the ratio deviation between tone intensity value and motion intensity value to generate the data dispersion index is as follows: The differential modulus between tone intensity value and motion intensity value is analyzed as the modal absolute error; the arithmetic sum of tone intensity value and motion intensity value is calculated as the modal total intensity; the modal total intensity and regularization constant are superimposed to generate a denominator term; the relative deviation ratio is generated by calculating the numerical relationship between the modal absolute error and the denominator term through relative ratio quantization; the relative deviation ratio is input into a preset exponential amplification function to amplify the input deviation signal nonlinearly, and the amplified value is output as the data dispersion index.

[0030] In this implementation scheme, the calculation of the data dispersion index aims to identify logical conflicts between multimodal data using statistical methods. The system first analyzes the differential modulus between tone intensity and motion intensity values, calculating the absolute value of their difference as the modal absolute error, which directly reflects the degree of deviation between the two dimensions. Simultaneously, to eliminate the influence of signal strength itself on error judgment (e.g., small differences at low intensities should not be considered conflicts), the system calculates the arithmetic sum of tone intensity and motion intensity values ​​as the total modal intensity. Subsequently, normalized relative ratio quantization is performed, and the total modal intensity is superimposed with a regularization constant to generate a denominator. Division is then used to calculate the numerical relationship between the modal absolute error and the denominator, generating the relative deviation ratio. Finally, to highlight anomalous states of severe misalignment, the relative deviation ratio is input into a preset exponential amplification function for nonlinear gain amplification. The calculation formula is as follows: ;in, The final output is the data dispersion index. The higher the value, the less reliable the data is. The preset exponential amplification ratio is used to adjust the output dynamic range of the dispersion index. The natural logarithm base is used to widen the numerical gap between low-conflict and high-conflict conditions by leveraging the growth characteristics of the exponential function. Modal absolute error characterizes the original discrepancy between acoustic and dynamic data. Total modal intensity, used as the benchmark for normalization; The regularization constant, typically a very small positive number (e.g., 0.001), primarily serves to prevent calculation overflow when both tone and motion intensity are zero (resulting in a zero denominator). It also smooths noise in the low-energy range. Using this formula, the system can quantify the degree of audio-visual mismatch into a highly sensitive numerical index. This allows it to output a high-dispersion index to suppress erroneous light triggering in abnormal situations such as someone shouting loudly without any physical movement (e.g., noise).

[0031] Specifically, the process of retrieving preset initial lighting parameters using the basic activity level as an index and substituting the data dispersion index into the inverse weighting function to calculate the credibility weight of the current data is as follows: An associated database containing the activity level field and the lighting parameter field is established. The basic activity level is used as the key to perform a matching query in the database, extracting the corresponding color temperature and brightness values ​​as initial lighting parameters. An inverse weighting calculation logic is constructed, setting a preset sensitivity coefficient and a baseline constant. The sensitivity coefficient and the data dispersion index are multiplied and modulated to generate an attenuation term. The baseline constant and the attenuation term are superimposed to generate a denominator. The credibility weight of the current data is output through the mapping between the baseline constant and the reciprocal of the denominator.

[0032] In this implementation scheme, during the parameter retrieval and weight calculation process of the confidence-based lighting effect correction module, a table-based parameter matching is first performed. The system establishes an associated database containing an activity level field and a lighting parameter field. This database is essentially a key-value pair mapping table that pre-stores expert strategies. By using the basic activity level as the key, a matching query is performed in the database to quickly extract the color temperature and brightness values ​​that theoretically best match the current scene atmosphere as initial lighting parameters. This step transforms discrete scene labels into specific physical control targets, but the parameters at this stage have not yet undergone environmental stability verification. To prevent control errors caused by data conflicts, the system constructs an inverse weighted calculation logic to quantify the confidence of the current data. The mathematical meaning of inverse weighting is that as the dispersion of the input signal (i.e., the degree of conflict) increases, its corresponding output weight should show a non-linear decreasing trend. The system sets a preset sensitivity coefficient and a reference constant, and performs product modulation on the sensitivity coefficient and the data dispersion index generated in the previous step to generate an attenuation term. The larger the value of the attenuation term, the stronger the inconsistency of the current sound and motion data. The baseline constant and the attenuation term are then superimposed to generate the denominator. The result is then used as the confidence weight of the current data by mapping the baseline constant to the reciprocal of this denominator. This calculation process achieves soft suppression of outliers, as shown in the following formula: ;in, The credibility weight of the current data is limited to a range of zero to one. The closer the value is to one, the more credible the data is. The preset baseline constant is used to control the convergence basis of the weighting function, and is usually set to 1.0; The preset sensitivity coefficient is used to adjust the system's sensitivity to data conflicts. This coefficient is determined experimentally, i.e., the reciprocal of the mean of the dispersion measured under standard noise conditions. The data dispersion index is calculated in the previous steps. Using this formula, when the dispersion index is extremely high, the denominator increases rapidly, causing the reliability weight to approach zero, thus achieving automatic filtering of low-quality instructions at the algorithm level.

[0033] Specifically, the process of generating target color temperature and target brightness values ​​by performing weighted fusion iterative calculations on initial lighting parameters and historical lighting parameters is as follows: The output value of the previous control cycle is read from the system register as the historical lighting parameter. Inertia retention weight is obtained through complementary calculations of unit values ​​and confidence weights. The initial lighting parameters and confidence weights are weighted and modulated to generate new state components. The historical lighting parameters and inertia retention weights are weighted and modulated to generate historical state components. Vector aggregation is performed on the new state components and historical state components, and the fused color temperature and brightness data are output as target color temperature and target brightness values, respectively.

[0034] In this implementation scheme, during the generation of target luminous efficacy parameters, the system employs weighted fusion iterative computation to achieve temporal smoothing of the control signal. This process is essentially a digital signal processing step based on the principle of first-order hysteresis filtering. First, the output value of the previous control cycle is read from the system register as the historical lighting parameters, representing the current physical state of the device. Next, the inertia retention weight is calculated by subtracting the confidence weight from the unit value (i.e., the value 1). This inertia retention weight reflects the system's tendency to maintain the current state: the lower the confidence, the greater the inertia. Subsequently, vector aggregation is performed on the color temperature and luminance data respectively. The system performs weighted modulation on the initial lighting parameters (the target to be changed) and the confidence weight to generate a new state component, and simultaneously performs weighted modulation on the historical lighting parameters (past states) and the inertia retention weight to generate historical state components. Finally, vector addition is performed on the new state component and the historical state component, outputting the fused color temperature and luminance data as the target color temperature and target luminance values, respectively. The calculation formula is as follows: ;in, :No. The target color temperature value or target brightness value output in each control cycle; : The credibility weight calculated in the current period; The initial lighting parameters retrieved in the current cycle, i.e., the theoretical target values; :No. The historical lighting parameters output in each control cycle represent the system state values ​​at the previous moment; 1 represents the unit constant of the full weight. Through this formula, the system implements a dynamic soft-switching mechanism: when the scene recognition is accurate (high confidence), the system responds quickly and approaches the initial lighting parameters; when the scene recognition is questionable (low confidence), the system mainly follows the historical parameters to maintain the stability of the light environment, thereby effectively solving the problem of light effect flickering caused by sensor jitter.

[0035] Specifically, the process of converting the target color temperature value and target brightness value into target pulse width modulation (PWM) values ​​and constructing an execution queue containing the current PWM value and the target PWM value is as follows: A preset color temperature mixing ratio algorithm is invoked, and the power allocation ratio between the warm color temperature channel and the cool color temperature channel is calculated based on the target color temperature value. The target duty cycle of each channel is mapped to the target brightness value, and then quantized into the target PWM value. The PWM value currently being output in the register at the current time is read as the current node, and the target PWM value is used as the termination node. A doubly linked list containing the start timestamp, the current node, and the termination node is constructed as the execution queue.

[0036] In this implementation scheme, the core of the pulse width modulation (PWM) value conversion and queue construction process in the timing interpolation drive module lies in mapping abstract luminous efficacy parameters into specific digital circuit control signals. The system first retrieves a preset color temperature mixing ratio algorithm, which is based on the principle of dual-color temperature mixing, i.e., synthesizing the target color temperature by adjusting the luminous power ratio of warm color temperature LEDs and cool color temperature LEDs. Based on the input target color temperature value, the power allocation ratio between the warm color temperature channel and the cool color temperature channel is calculated. This step ensures that the synthesized light color matches the expected emotional atmosphere. Next, the target duty cycle of each channel is mapped to the target brightness value and quantized into a target PWM value. Pulse width modulation (PWM) is a technique that controls the output power of an analog circuit by changing the high-level duration (i.e., duty cycle) of a digital pulse signal. The calculation formula for this conversion process is as follows: ; ;in, : Target pulse width modulation value for the cool color temperature channel; : Target pulse width modulation value for the warm color temperature channel; The target brightness value generated in the previous steps; The target color temperature value generated in the previous steps; The lowest warm color temperature parameter supported by the lighting hardware, such as 2700; The highest cool color temperature parameter supported by the lighting hardware, such as 6500; A preset hardware calibration coefficient is used to compensate for differences in luminous efficiency between different batches of LED beads. This coefficient is determined through factory color calibration testing. After numerical quantization, the system reads the pulse width modulation value being output from the register at the current moment as the current node and uses the calculated target pulse width modulation value as the termination node. The system constructs a doubly linked list containing a start timestamp, the current node, and the termination node as a queue to be executed. The doubly linked list is a data structure that allows the system to quickly insert new high-priority instructions or delete expired instructions during changes in luminous efficiency, ensuring the real-time performance and flexibility of the control logic.

[0037] Specifically, the process of generating a transition step sequence between the current and target values ​​using a linear interpolation algorithm and sequentially outputting pulse width modulation control instructions according to a preset clock cycle is as follows: Calculate the numerical difference between the termination node and the current node, obtain the preset total transition duration, and derive the single-step change slope by analyzing the ratio of the numerical difference to the number of clock cycles contained in the total transition duration; initialize the accumulator, and when the rising edge of each preset clock cycle arrives, perform cumulative iteration on the output value of the previous cycle and the single-step change slope to generate the intermediate step value at the current moment; convert the intermediate step value into a binary control signal, write it into the driver register through a general-purpose input / output interface, and output the pulse width modulation control instruction.

[0038] In this implementation scheme, the goal of generating a continuous and smooth intermediate state through an algorithm during the linear interpolation and command output process is to solve the problem of abrupt changes in light effect. The system first calculates the numerical difference between the termination node and the current node and obtains a preset total transition time, which is typically set based on the human eye's adaptation to changes in light. Subsequently, the single-step change slope is derived by analyzing the ratio of the numerical difference to the number of clock cycles contained in the total transition time. This step essentially calculates the tiny step size by which the PWM value should increase or decrease within each clock cycle, as shown in the following formula: ;in, The calculated slope of the single-step change; this value may be a floating-point number. : The value of the termination node in the queue to be executed (i.e., the target PWM value); : Current node value (i.e., current PWM value); The preset total transition time, for example, 3 seconds, determines how fast the light effect changes. The system's preset clock cycle frequency is the number of updates performed per second. Next, the accumulator is initialized; this is a memory variable used to store intermediate calculation results. At the rising edge of each preset clock cycle, the system performs an iterative accumulation operation, adding the output value of the previous cycle to the single-step change slope to generate the intermediate step value at the current moment. This iterative process achieves a linear gradual change in value, as shown in the following formula: ;in, :No. The intermediate step value calculated over one clock cycle; :No. The value over one clock cycle (initial value) ; The previously calculated single-step change slope is used. Finally, the system converts the calculated intermediate step values ​​into binary control signals and writes them to the driver register via a general-purpose input / output interface (GPIO). The driver register is the physical unit that directly controls the on / off state of the hardware circuit. As the register value is periodically refreshed, the pulse width output by the LED driver circuit gradually changes, thereby achieving a seamless and smooth transition of lighting effect in physical space.

[0039] In summary, this application has at least the following effects:

[0040] The intelligent landscape lighting adjustment system based on the fusion analysis of tone and motion states achieves accurate quantification and authenticity verification of scene atmosphere by normalizing and extracting tone emotion and motion intensity and performing collaborative interactive calculations. It utilizes the interactive collaborative gain coefficient and data dispersion index to effectively shield the interference of single-modal environmental noise. At the same time, it combines confidence-based light effect correction and time-series interpolation driving technology based on inverse weighting to dynamically balance the current response and historical inertia according to data credibility, generating continuous and smooth pulse width modulation control commands. While ensuring that the lighting logic is highly matched with the actual psychological state of the crowd, it completely solves the problems of false triggering and sudden changes in light effect caused by signal jitter or logic conflict in traditional control systems, significantly improving the intelligent interactive stability and visual comfort of landscape lighting.

[0041] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0042] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0043] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0044] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0045] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0046] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A landscape lighting intelligent adjustment system based on the fusion analysis of tone and motion state, characterized in that, Includes the following modules: The feature extraction and quantization module is used to perform short-time Fourier transform on the acquired audio frame sequence, extract the fundamental frequency mean and frequency fluctuation variance in the frequency domain as tone feature components, and perform normalization processing to generate tone intensity values. At the same time, it performs differential operation on the acquired moving target coordinate sequence, extracts the displacement velocity mean and direction change rate per unit time as motion feature components, and performs normalization processing to generate motion intensity values. The collaborative interaction calculation module is used to linearly weight and sum the tone intensity value and motion intensity value to obtain the interactive collaborative gain coefficient that represents the sound-motion synchronization, determine the basic activity level of the current scene, and calculate the ratio deviation between the tone intensity value and the motion intensity value to generate the data dispersion index. The confidence level light effect correction module is used to retrieve preset initial lighting parameters using the basic activity level as an index, and substitute the data dispersion index into the inverse proportional weighting function to calculate the confidence weight of the current data. It performs weighted fusion iterative calculation on the initial lighting parameters and historical lighting parameters to generate target color temperature value and target brightness value. The timing interpolation driver module is used to convert the target color temperature value and the target brightness value into target pulse width modulation values, construct an execution queue containing the current pulse width modulation value and the target pulse width modulation value, generate a transition ladder sequence between the current and target values ​​through a linear interpolation algorithm, and output pulse width modulation control commands sequentially according to a preset clock cycle.

2. The intelligent landscape lighting adjustment system based on tone and motion state fusion analysis according to claim 1, characterized in that: The specific process of performing a short-time Fourier transform on the acquired audio frame sequence, extracting the fundamental frequency mean and frequency fluctuation variance in the frequency domain as tone feature components, and then performing normalization processing to generate tone intensity values ​​is as follows: The audio frame sequence is segmented and windowed using the Hanning window function, and a fast Fourier transform is performed to obtain the spectral amplitude spectrum of each frame. The fundamental frequency peak point is located using a spectral peak search algorithm. The average value of the fundamental frequency peak points of multiple consecutive frames is analyzed as the fundamental frequency mean. At the same time, the second-order central moment of the fundamental frequency peak point on the time axis is quantized as the frequency fluctuation variance. The fundamental frequency mean and frequency fluctuation variance are projected onto a preset emotional feature mapping interval. The projected feature data is compressed into the range of zero to one using the Sigmoid activation function. The compressed fundamental frequency data and variance data are then merged through a weighted aggregation operation to output the tone intensity value that represents the emotional activity of the voice.

3. The intelligent landscape lighting adjustment system based on tone and motion state fusion analysis according to claim 1, characterized in that: The specific process of performing differential operations on the collected coordinate sequence of the moving target, extracting the mean displacement velocity and rate of change of direction per unit time as motion feature components, and then performing normalization processing to generate motion intensity values ​​is as follows: Perform first-order differential operations on adjacent coordinate points in the coordinate sequence of the moving target, analyze the Euclidean distance to obtain the instantaneous velocity vector magnitude, and calculate the arithmetic mean of all instantaneous velocity vector magnitudes within a preset time window as the displacement velocity mean. Simultaneously, the geometric angle relationship between adjacent instantaneous velocity vectors is extracted, and the change in tangential angle of the motion trajectory is obtained analytically through the inverse cosine function. The cumulative value of the angle change per unit time is used as the rate of change of direction. The mean displacement velocity and the rate of change of direction are mapped to a standard dimensionless interval by the maximum-minimum standardization. The standardized mean velocity is taken as the active component and the standardized rate of change of direction is taken as the entropy component. The active component and the entropy component are weighted and fused to output the motion intensity value that represents the intensity of the group behavior.

4. The intelligent landscape lighting adjustment system based on tone and motion state fusion analysis according to claim 1, characterized in that: The specific process of determining the basic activity level of the current scene by linearly weighting and summing the tone intensity value and the motion intensity value to obtain the interactive cooperative gain coefficient representing the acoustic-motor synchronization is as follows: The preset environmental bias weight coefficients are retrieved, and priority weights are assigned to the tone intensity value and motion intensity value respectively. The weighted sum of the two is analyzed as the initial cooperative modulus. The tone intensity value and motion intensity value are coupled and modulated to generate a coupling gain term. The initial cooperative modulus and the coupling gain term are superimposed to generate an interactive cooperative gain coefficient. A multi-level tiered threshold decision logic is constructed, which compares the interaction collaboration gain coefficient with the preset quiet threshold, interaction threshold and carnival threshold in sequence. When the coefficient falls into different threshold ranges, the corresponding discretized state identifier is output as the basic activity level of the current scene.

5. The intelligent landscape lighting adjustment system based on tone and motion state fusion analysis according to claim 4, characterized in that: The specific process for calculating the ratio deviation between tone intensity values ​​and motion intensity values ​​to generate a data dispersion index is as follows: The difference modulus between the tone intensity value and the motion intensity value is analyzed as the modal absolute error, and the arithmetic sum of the tone intensity value and the motion intensity value is calculated as the modal total intensity. The total modal intensity and the regularization constant are superimposed to generate a denominator term. The relative deviation ratio is generated by quantifying the numerical relationship between the absolute modal error and the denominator term through relative ratio quantification. The relative deviation ratio is input into a preset exponential amplification function, which performs nonlinear gain amplification on the input deviation signal, and outputs the amplified value as the data dispersion index.

6. The intelligent landscape lighting adjustment system based on tone and motion state fusion analysis according to claim 1, characterized in that: The specific process of retrieving preset initial lighting parameters using the basic activity level as an index, and substituting the data dispersion index into the inverse proportional weighting function to calculate the credibility weight of the current data is as follows: Establish an associated database containing an activity level field and a lighting parameter field. Use the basic activity level as the key to perform a matching query in the database and extract the corresponding color temperature value and brightness value as the initial lighting parameters. Construct an inverse weighted calculation logic, set a preset sensitivity coefficient and a benchmark constant, perform product modulation on the sensitivity coefficient and the data dispersion index to generate an attenuation term, perform superposition operation on the benchmark constant and the attenuation term to generate a denominator, and output the confidence weight of the current data through the mapping between the benchmark constant and the reciprocal of the denominator.

7. The intelligent landscape lighting adjustment system based on tone and motion state fusion analysis according to claim 6, characterized in that: The specific process of generating target color temperature and target brightness values ​​by performing weighted fusion iterative calculations on initial and historical lighting parameters is as follows: The output value of the previous control cycle is read from the system register as the historical lighting parameter. The inertia retention weight is obtained by complementary calculation of unit value and confidence weight. The initial lighting parameters and the confidence weight are weighted and modulated to generate new state components, and the historical lighting parameters and the inertia retention weight are weighted and modulated to generate historical state components. Vector aggregation is performed on the new state components and the historical state components, and the fused color temperature data and brightness data are output as the target color temperature value and the target brightness value, respectively.

8. The intelligent landscape lighting adjustment system based on tone and motion state fusion analysis according to claim 1, characterized in that: The specific process of converting the target color temperature value and target brightness value into target pulse width modulation values, and constructing an execution queue containing the current pulse width modulation value and the target pulse width modulation value is as follows: The preset color temperature mixing ratio algorithm is retrieved, and the power allocation ratio between the warm color temperature channel and the cool color temperature channel is calculated based on the target color temperature value. The target duty cycle of each channel is mapped to the target brightness value and quantized into the target pulse width modulation value. Read the pulse width modulation value being output in the register at the current time as the current node, and take the target pulse width modulation value as the termination node. Construct a doubly linked list containing the start timestamp, the current node, and the termination node as the queue to be executed.

9. The intelligent landscape lighting adjustment system based on tone and motion state fusion analysis according to claim 8, characterized in that: The specific process of generating a transition step sequence between the current and target values ​​using a linear interpolation algorithm, and then sequentially outputting pulse width modulation control commands according to a preset clock cycle, is as follows: Calculate the numerical difference between the termination node and the current node, obtain the preset total transition time, and analyze the single-step change slope by the ratio of the numerical difference to the number of clock cycles included in the total transition time. Initialize the accumulator. When the rising edge of each preset clock cycle arrives, perform an accumulation iteration on the output value of the previous cycle and the single-step change slope to generate the intermediate step value at the current moment. The intermediate step values ​​are converted into binary control signals and written to the drive register through a general-purpose input / output interface to output pulse width modulation control instructions.

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