Method for monitoring audio frequency response of meditation disk based on Clarii graph

Abstract

The invention discloses a meditation disk audio frequency response monitoring method based on a Clarii graph, and relates to the technical field of information, a piezoelectric actuator is used for generating a multi-frequency superposition sound wave signal, fluorescent powder on the surface of a vibration disk is driven to form a composite Clarii graph, the frequency value and the amplitude value of each frequency component are identified in real time, and the node line distribution pattern is analyzed. When it is detected that the phase conflict intensity of the node line overlapping area exceeds the standing wave stability critical value, the node ambiguity is quantized by extracting the gray gradient change range of the node line boundary, and the boundary undefinition is evaluated according to the boundary point position distance. In combination with user alpha-wave frequency band heart rate stability data, the driving frequency configuration of the ceramic vibration disc is dynamically adjusted, meanwhile, the LED light projection area and the color temperature switching rate are optimized, and clear presentation and intelligent control of the Clariy graph in the multi-frequency environment are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, in particular to a meditation plate audio response monitoring method based on Kranich pattern. BACKGROUND

[0002] Kranich pattern formed by acoustic wave vibration on the surface of a solid plays an important role in meditation assistance, acoustic research and artistic creation as a direct physical phenomenon display method. This technology, which forms a geometric pattern by scattering fine particulate matter on the vibration plane and using the standing wave effect of the acoustic wave to make the particles gather at the node position, can convert abstract acoustic frequency into visual spatial structure.

[0003] Traditional Kranich pattern generation mainly relies on single frequency acoustic wave excitation, which can produce clear and stable basic patterns, but this method has obvious shortcomings in representing the complexity and richness of acoustic waves. When creating more complex and diverse visual effects, single frequency excitation cannot meet the demand for pattern diversity and visual hierarchy, limiting its application potential in scenarios requiring fine state differentiation.

[0004] When multiple acoustic waves of different frequencies act on the same vibration surface at the same time, each frequency component will form its own independent standing wave mode in space, and these modes will overlap in space to produce complex interference phenomena. For example, in meditation assistance applications, when three acoustic waves of different frequencies are used to excite at the same time, the originally clear single-frequency node line boundary begins to blur, and the particle gathering position in the overlapping area becomes unpredictable, making the pattern features originally used to distinguish different meditation state levels unclear. SUMMARY

[0005] To overcome the shortcomings of the prior art, the present application provides a meditation plate audio response monitoring method based on Kranich pattern, which solves the problems in the background art.

[0006] To achieve the above purpose, the present application is realized by the following technical scheme: a meditation plate audio response monitoring method based on Kranich pattern, comprising: S1: Obtain the acoustic wave signal source of the ceramic vibration disc surface, after Fourier transform, extract the initial frequency component, and obtain the frequency value, amplitude value and frequency number by spectrum decomposition and fusion, and obtain the optimized frequency component set after filtering and optimization; S2: According to the optimized frequency component set, the node line distribution form of the composite Kranich pattern is obtained to determine the frequency overlap layer number, and the vibration intensity detection is fused to obtain the optimized pattern level frequency overlap layer number; S3: Extract the overlapping region coordinates from the wave node line distribution form, evaluate the phase conflict intensity of the wave overlapping region combined with the frequency overlap layer number, when the phase conflict intensity exceeds the preset standing wave stability critical value, through gray scale gradient analysis, the wave node ambiguity value is obtained; S4: Extract the boundary points from the composite clairvoyant figure, determine the spacing value of adjacent boundary points according to the boundary points, and evaluate the boundary ambiguity through the spacing value of the boundary points; S5: Evaluate the accuracy of pattern recognition under different frequency combinations through the boundary ambiguity and the frequency overlap layer number, determine the recognizable dynamic change curve and the frequency layer number range that can be safely adjusted according to the change relationship of the accuracy with the frequency overlap layer number; S6: Obtain the heart rate stability corresponding to the user's alpha wave frequency band, identify the curve change rate of the frequency overlap layer number according to the recognizable dynamic change curve, and adjust the frequency number and the frequency value combined with the boundary ambiguity, so as to optimize the pattern level distinction clarity, and determine the driving frequency configuration matrix of the ceramic vibration disc according to the adjusted frequency number and the frequency value, and obtain the updated driving frequency configuration matrix and integrated control parameters after the LED color temperature switching rate is corrected.

[0007] The present application has the following beneficial effects: The present application discloses a meditation disc audio response monitoring method based on clairvoyant figure, which solves the technical problems of wave node line overlapping ambiguity and low pattern recognition accuracy of traditional clairvoyant figure in a multi-frequency environment. A multi-frequency superimposed acoustic wave signal is generated by a piezoelectric driver to drive the fluorescent powder on the surface of the vibration disc to form a composite clairvoyant figure, and the frequency value and amplitude value of each frequency component are identified in real time and the wave node line distribution form is analyzed. When the phase conflict intensity of the wave node line overlapping region exceeds the standing wave stability critical value, the present application quantifies the wave node ambiguity by extracting the boundary gray scale gradient change range, and evaluates the boundary ambiguity according to the spacing between the boundary points. Combined with the heart rate stability data of the user's alpha wave frequency band, the driving frequency configuration of the ceramic vibration disc is dynamically adjusted, and the LED light projection area and color temperature switching rate are optimized, realizing clear presentation and intelligent control of the clairvoyant figure in a multi-frequency environment, and improving the pattern level distinction clarity and recognition accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0008] Fig. 1 The present application is a method flowchart; Fig. 2 The present application is a part of the schematic diagram; Fig. 3 The present application is a dynamic trend chart of the accuracy changing with the frequency overlap layer number under different frequency combinations. DETAILED DESCRIPTION

[0009] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0010] Please refer to Figs. 1 to 3 The present application provides a meditation disc audio response monitoring method based on a Kranz graph, comprising, S1: Obtain the sound wave signal source of the ceramic vibration disc surface, extract the initial frequency component after Fourier transform, obtain the frequency value, amplitude value and frequency number through spectrum decomposition fusion, and obtain the optimized frequency component set after filtering optimization; The sound wave signal source of the ceramic vibration disc surface is obtained through a piezoelectric pickup sensor, the original frequency spectrum signal is obtained after Fourier transform, and the initial frequency component is obtained from the original frequency spectrum signal. Specifically, the time domain signal is converted into a frequency domain signal to reveal each frequency component contained therein.

[0011] After spectrum decomposition fusion of the initial frequency component, the frequency value and amplitude value of each frequency component in the initial frequency component are obtained to determine the frequency number. If the frequency number exceeds the preset threshold, it means that the signal is complex or there is too much noise, and filtering optimization is needed. Then, the original frequency spectrum signal is subjected to a band-pass filtering operation and amplitude normalization processing to adjust the amplitude value and obtain the optimized frequency component set, only the frequency components within the effective frequency band are retained, thereby suppressing high-frequency noise and low-frequency interference signals. Specifically, first, the Fourier transform result is smoothed by using sliding average or Gaussian filtering to eliminate sharp burrs, then local maximum points are detected to extract peak positions and corresponding amplitudes, and if the frequency interval between two peaks is less than the minimum resolution, it is considered as the same frequency cluster, and the remaining independent peak number after fusion is the frequency number; Based on the optimized frequency component set, the energy and distribution characteristics are fused to analyze the abnormal frequency band, and the frequency component set with service attributes is generated.

[0012] The specific steps of determining the abnormal frequency band are as follows: based on the optimized frequency component set, the frequency value and the amplitude value are taken as input features, and a preset sound wave detection model, i.e., a machine learning model, is combined for fusion analysis; the machine learning model includes frequency distribution feature parameters and determination rules for different detection scenarios (such as sound field stability detection, equipment resonance identification, or structure anomaly monitoring). In the fusion process, the system calculates the energy concentration degree, the distribution deviation degree, and the correlation of adjacent frequency bands of each frequency component to evaluate the balance degree of the frequency energy in the overall spectrum domain. When the calculation result shows that the energy concentration degree of a certain frequency band is lower than a threshold value or the frequency distribution deviation degree exceeds a preset range, it is determined that the frequency band has abnormal frequency distribution. Subsequently, the frequency components with abnormal characteristics are labeled according to the corresponding business model to obtain a frequency component set with business semantic information, which represents the feature state and abnormal type of the sound wave signal in different business scenarios.

[0013] The sound wave signal source is a composite sound wave signal emitted by a piezoelectric driver, which is essentially a multi-frequency signal composed of multiple sine waves with different frequencies, phases, and amplitudes. The initial frequency component refers to the set of all significant frequency components extracted from the original signal, which has not been filtered or fused and optimized.

[0014] The frequency value refers to the center frequency of each frequency component; the amplitude value is the energy intensity corresponding to the frequency; and the frequency number is the number of significant frequencies retained after fusion. The optimized frequency component set is a set of representative main frequency components retained after filtering, denoising, amplitude normalization, and abnormal peak correction of the original frequency spectrum signal. It contains the frequency value and the corrected amplitude value of each effective frequency component, and is used to reflect the real and stable energy distribution characteristics in the sound wave signal.

[0015] In this embodiment, the sound wave signal source on the surface of the ceramic vibration disc is accurately acquired by the piezoelectric pickup sensor, and the time domain signal is converted into a frequency domain signal by Fourier transform, which can completely extract the initial frequency component and avoid loss of frequency information in the original signal, providing comprehensive and accurate basic data for subsequent analysis and solving the problem of incomplete extraction of frequency components in traditional signal acquisition methods.

[0016] Spectral decomposition and fusion of the initial frequency component can accurately obtain the frequency value, amplitude value, and frequency number of each frequency component. By judging whether the frequency number exceeds a preset threshold value to determine whether to filter and optimize, and then through band-pass filtering, amplitude normalization, and other processing, the optimized frequency component set is obtained, which effectively suppresses high-frequency noise and low-frequency interference, ensures the authenticity and stability of the frequency component, and solves the problem of inaccurate frequency analysis caused by complex signals or excessive noise.

[0017] The preset machine learning model is fused to analyze energy and distribution characteristics of the optimized frequency component set, calculate parameters such as energy concentration degree and distribution deviation degree to determine an abnormal frequency band, and also can mark the service attribute of the abnormal frequency component, so that the frequency component set has service semantic information, the precision of identifying the sound wave signal characteristics and the abnormal type in different service scenarios is realized, and the scene adaptability and practicability of the sound frequency response monitoring are improved.

[0018] The service attribute refers to the information label with service semantics of the frequency component set. In the whole S1 step, from signal acquisition, frequency extraction, optimization processing to abnormality determination, a complete technical chain is formed, the technical features of each link work together, which not only ensures the accuracy of frequency analysis, but also realizes the adaptation of service scenarios, breaks through the limitation of traditional monitoring methods which only pay attention to the signal itself and lack business association and precise optimization, provides reliable data support for subsequent monitoring links of the meditation disc sound frequency response based on the Kranii figure, and improves the scientificity and effectiveness of the whole monitoring scheme.

[0019] In a preferred embodiment of the present application, S2: according to the optimized frequency component set, the wave node line distribution form of the composite Kranii figure is obtained to determine the frequency overlap layer number, and the vibration intensity detection is fused to obtain the optimized pattern layer frequency overlap layer number. The frequency value in the optimized frequency component set is input as a driving signal into the ceramic vibration disc, so that the fluorescent powder particles on the surface layer of the disc are visually migrated under the excitation of sound waves, to form a multi-standing wave mode, when the multi-standing wave mode exists at the same time, the fluorescent powder particles on the surface layer of the disc produce a fluorescent powder response under the action of sound pressure gradient, that is, the powder is gathered in the node area, sparse in the abdomen area and emits a fluorescent signal, a plurality of distribution maps formed under different frequency excitations are recorded by an optical sensor, the plurality of distribution maps under different frequency modes are registered, normalized and brightness weighted synthesized through image superposition algorithm, to obtain a composite Kranii figure, according to the composite Kranii figure, the wave node line distribution form of the composite Kranii figure is determined, which is used to reflect the overall wave node line distribution form under the superposition condition of multi-frequency sound field. The plurality of distribution maps refer to the standing wave pattern distribution images of the fluorescent powder particles on the surface of the ceramic vibration disc formed under the action of a single frequency sound wave, which are recorded by the optical sensor under different frequency excitation conditions.

[0020] Based on the composite Kranii figure, the brightness gradient distribution is extracted through gray scale processing and edge detection to obtain the center and edge curve of the powder dense area, and the trend and intersection state of the wave node line are analyzed according to the curve continuity and branch structure to obtain the complete wave node line distribution form of the composite figure.

[0021] According to the wave node distribution morphology, the boundary gray scale gradient change range is extracted, and the composite Kranie pattern is regionally divided to obtain the wave node intersection density in each region. By analyzing the corresponding relationship between the boundary gray scale gradient change range and the wave node intersection density, the frequency overlap number of the pattern level is determined. The boundary gray scale gradient change range is a parameter range for quantifying the gray scale transition characteristics of the wave node edge in the composite Kranie pattern, specifically refers to the numerical interval composed of the average change amplitude and the standard deviation of the gray scale difference of adjacent pixel points in the boundary region of the wave node, and is used to reflect the transition intensity of the wave node edge from light to dark, and can measure the clarity and energy concentration of the pattern.

[0022] Specifically, after obtaining the wave node distribution morphology, the system performs gray scale gradient analysis on the boundary region thereof, extracts the boundary gray scale gradient change range by calculating the average change amplitude and the standard deviation range of the gray scale difference of adjacent pixel points, and the change range reflects the transition intensity of the wave node edge from light to dark, and can be used to measure the clarity and energy concentration of the pattern. The smaller the boundary gray scale gradient change range is, the clearer the boundary transition is, and the more stable the standing wave node is.

[0023] The frequency overlap number of the pattern level refers to the number of levels formed by the superposition of different frequency patterns in the composite Kranie pattern, and represents the layered interference structure formed by different frequency components in space.

[0024] The wave node intersection density in each region refers to the number of wave node intersection points per unit area, and is used to reflect the interference complexity of different frequency wave fields in space. The higher the intersection density is, the more frequency patterns act simultaneously in the region, forming higher-level energy superposition.

[0025] The system finds the regions with sharp gray scale transition and significant increase in intersection density by corresponding comparison between the boundary gray scale gradient change range and the intersection density distribution. The boundaries of these regions represent the boundary positions of the frequency interference bands. Each frequency interference band refers to an independent interference energy band formed by a specific frequency group on the disc surface, and the internal wave form is relatively stable and the energy is concentrated. There is a significant difference in gray scale and density between different interference bands. The boundary interval of each frequency interference band is the transition region where the gray scale gradient changes from rising to falling and the intersection density changes from high to low between adjacent bands. By traversing the gray scale gradient distribution and the intersection density mapping relationship of the entire image, the system can label these boundary intervals layer by layer, and count the number of independent interference layers to determine the frequency overlap number of the pattern level. The pattern level essentially describes the layered structure of the composite pattern from low frequency patterns to high frequency patterns in the depth direction, and each layer represents an independent frequency interference plane.

[0026] According to the frequency overlapping layer fusion vibration intensity detection, wherein the vibration intensity detection collects intensity data from the surface of the vibrating disc, judges and identifies the density distribution of the fluorescent powder particles, and obtains a density distribution set; According to the frequency overlapping layer fusion vibration intensity detection, it means that the system combines the pattern layer information with the real-time vibration intensity data to verify the rationality of the energy distribution of each frequency layer. The vibration intensity data comes from the acceleration sensor or laser vibration meter installed on the surface of the ceramic vibrating disc, which is used to measure the vibration acceleration amplitude, power spectral density and displacement amplitude at different positions. These data reflect the actual vibration intensity of the disc surface under the superposition of each frequency. By comparing the vibration energy of each layer corresponding region with the powder density, the system judges the uniformity of the powder distribution in the high vibration area and the low vibration area, and identifies the density distribution of the fluorescent powder particles.

[0027] The judgment process of the density distribution of the fluorescent powder particles is as follows: the system performs spatial registration on the vibration intensity distribution map and the brightness map of the fluorescent powder, and calculates the correlation coefficient of the vibration energy and the powder brightness of each pixel point after the coordinate systems of the two maps are unified. When the correlation coefficient is higher than the threshold value, it indicates that the powder in this area is stable, and when the correlation coefficient is lower than the threshold value, it indicates that the powder distribution is disturbed or the local vibration is uneven. The density parameter set of all regions forms the density distribution set, which is used to describe the spatial distribution state of the powder on the entire disc surface and the energy corresponding relationship.

[0028] Based on the density distribution set, the spectral response of the particles in different regions is analyzed to obtain the peak shift amount, and the stress state between the particles is analyzed according to the peak shift amount to obtain the interaction force value reflecting the constraint tightness of the particles under the action of sound pressure; After obtaining the density distribution set, the system performs spectral response analysis on the fluorescent powder in different regions, irradiates the disc surface with an excitation light source, and collects the emission spectrum of the powder, and compares the spectral peak position and intensity difference of the particles in different regions.

[0029] The spectral peak difference refers to the difference degree of the spectral peak intensity or peak position in the emission spectrum curve of the fluorescent powder in different regions.

[0030] The peak shift amount represents the shift amount of the spectral peak from the original wavelength to the red shift or blue shift after the microstructure of the fluorescent powder particles changes (such as lattice strain and particle spacing change) under the action of sound wave. The peak shift amount is obtained by subtracting the peak position in the static or reference state from the current detected peak position. When it exceeds 0, it indicates red shift (particle spacing increases), and when it does not exceed 0, it indicates blue shift (particle spacing decreases). The peak shift amount directly reflects the change of local strain, which can be expressed by the photo-elastic relationship as follows: , is the local strain, is the peak shift amount, peak position in the stationary or reference state, photo-elastic constant of the material (indicating the proportional coefficient of wavelength change and strain); In the acoustic field, the powder particles are subjected to periodic acoustic pressure and micro-strain, both of which satisfy the stress-strain relationship: , is the equivalent stress between particles, and E is the Young's modulus of the particle material; By multiplying the equivalent stress between particles and the particle contact area, the interaction force value reflecting the tightness of the particles under the action of acoustic pressure is obtained, which is used to evaluate the stress intensity and constraint stability between the fluorescent powder particles under the action of acoustic waves, to determine whether the energy distribution of the acoustic field is balanced and the pattern structure is stable.

[0031] When the interaction force value exceeds the preset threshold, the frequency component set is adjusted and optimized by Fourier transform, that is, the frequency components corresponding to the abnormal area are re-input into the frequency spectrum analysis module, and inverse Fourier transform and reconstruction operations are performed on them to correct the superposition ratio and phase difference between frequencies, so as to obtain the adjusted frequency set. This process is equivalent to fine-tuning the amplitude and phase relationship of each component in the frequency domain to restore the energy balance of the acoustic field.

[0032] After the adjustment of the frequency set is completed, the system recalculates the new node structure formed by the superimposed frequency pattern, generates a new composite Kranig pattern, and performs the same gray gradient and hierarchical analysis as before to obtain the optimized pattern hierarchical frequency overlap layer number.

[0033] Multi-standing wave mode refers to multiple spatial standing wave patterns produced by the superposition of different frequency components on the disc surface, each mode corresponding to a specific node and antinode distribution.

[0034] Each distribution map reflects the spatial migration state of the powder under an independent frequency mode.

[0035] In this embodiment, the optimized frequency component set is input as a driving signal into the ceramic vibration disc, and the visual migration of the fluorescent powder under the excitation of acoustic waves forms a multi-standing wave mode. Combined with the optical sensor recording distribution map and the image superposition algorithm to synthesize a composite Kranig pattern, the node line distribution pattern under the superposition of multi-frequency acoustic field can be intuitively presented, solving the problem that a single frequency mode cannot reflect the state of a multi-frequency superimposed acoustic field, and providing a clear and visual basis for subsequent analysis.

[0036] Based on the composite Kranig pattern, gray scale processing and edge detection are carried out, and the corresponding relationship between the boundary gray scale gradient change range and the node line intersection density is determined to determine the frequency overlap layer number, which can accurately quantify the layered interference structure formed in space by different frequency modes, avoiding the defect of ambiguous frequency superposition level judgment in traditional methods, and improving the accuracy of frequency overlap layer number identification.

[0037] The fusion vibration intensity detection combines pattern hierarchical information and vibration intensity data, and through spatial registration of the vibration intensity distribution diagram and the fluorescent powder brightness diagram, the fluorescent powder particle density distribution can be accurately identified, the rationality of the energy distribution of each frequency layer can be verified, the problem that whether the energy distribution is reasonable cannot be verified only by relying on the graph is solved, and the reliability of the frequency layer energy analysis is ensured.

[0038] Spectrum response analysis is performed on the density distribution set, the interaction force value between particles is derived through the peak shift amount, the energy distribution balance of the sound field and the pattern structure stability can be accurately evaluated, when the interaction force value is abnormal, the frequency component set is adjusted and optimized through Fourier transform, and the composite clari pattern is regenerated, the dynamic optimization of the frequency and the pattern is realized, the closed loop of "driving-detection-analysis-adjustment" is formed, the limitation that the traditional monitoring only stays in the observation level and lacks active optimization is broken through, and the accuracy and stability of the meditation plate sound frequency response monitoring are significantly improved.

[0039] In a preferred embodiment of the present application, S3: the overlapping region coordinates are extracted from the wave node line distribution form, the phase conflict intensity of the wave overlapping region is evaluated in combination with the frequency overlapping layer number, when the phase conflict intensity exceeds the preset standing wave stability critical value, the wave node ambiguity value is obtained through gray scale gradient analysis; The standing wave stability critical value is set through the mean standard deviation method; The overlapping region coordinates are obtained from the wave node line distribution form, the wave node line distribution of each frequency layer is projected onto the same coordinate plane, the number of times that each overlapping region coordinate is covered by different frequency layers at the same time is counted, and the counting result is normalized to obtain the phase conflict intensity; the phase conflict intensity is used to reflect the interference energy intensity of the phase superposition difference between different frequency layers in space; The overlapping region coordinates are obtained from the wave node line distribution form of the composite clari pattern, which means that the system locates the intersection or overlapping region of all wave node lines after identifying the wave node line direction and branch structure. The system first converts the wave node line image into a binary topological graph and identifies the continuous pixel path of each wave node line. By detecting the spatial distance between different wave node lines, when the distance between two or more wave node lines is less than a preset threshold in the same region pixel range, the system marks the pixel center point as an overlapping point. Then, the adjacent overlapping points are spatially aggregated through a clustering algorithm to obtain the geometric center coordinates and boundary rectangle range of each overlapping block, thereby obtaining the overlapping region coordinate set.

[0040] The overlapping region coordinates represent the spatial position set of the strongest interference and the most severe phase change of the multi-frequency standing wave on the disc surface.

[0041] The nodal line distribution of each frequency layer refers to the geometric distribution form of the standing wave node line (nodal line) formed on the surface of the vibrating disc under different driving frequencies. Each frequency layer represents a wave node structure under the excitation of an independent frequency component, and the position, spacing and direction of the nodal line are different, reflecting the sound field mode corresponding to the frequency. When multiple frequencies act together, the nodal lines of these different frequency layers superimpose on the disc surface to form the overall interference pattern of the composite Kranie.

[0042] If the phase conflict intensity exceeds the preset standing wave stability threshold, and combined with the gray scale gradient analysis, the nodal ambiguity value is obtained; , The nodal ambiguity value represents the attenuation degree of the nodal boundary definition at the coordinates (x, y); The gray scale gradient module value under the ideal or reference state; The gray scale gradient module value of the current nodal line reflects the intensity of the boundary brightness change; The normalized phase conflict intensity is used to correct the local interference sensitivity of the nodal ambiguity.

[0043] The standing wave stability threshold refers to the threshold value determined by the maximum allowable value of the phase conflict intensity before the nodal structure loses stability under different frequency combinations in multiple experiments. The acquisition method is: in the process of gradually increasing the frequency superposition layer, monitor the Kranie pattern boundary ambiguity, when the boundary changes from clear state to diffuse state, record the phase conflict intensity value at this time as the standing wave stability threshold, which is used to determine whether the current sound field is in the stable resonance interval.

[0044] The phase shift distribution is obtained from the boundary gray scale gradient change range, and the stability index of the overlapping area is obtained by the integral ratio of the phase shift distribution and the nodal ambiguity value; Wherein, The stability index is used to measure the overall ratio of phase change to ambiguity in the overlapping area. The smaller the value, the more stable the phase change and the structure. The local phase change amplitude, The area element, The nodal ambiguity total is used to normalize the phase change to obtain a comparable stability index. The stability index total is;( , The starting boundary coordinate point of the overlapping area is, , The end boundary coordinate point of the area is; The gray scale peak position change of the same nodal line edge at adjacent time is analyzed, the gray scale peak displacement is calculated by time series difference, and the gray scale peak displacement is converted into phase difference , is the phase difference, is the local sound wave wavelength, is the circular constant, is the gray scale peak displacement, thereby forming a phase shift distribution, which represents the phase shift law of the nodal line with time or frequency variation.

[0045] A function mapping of the stability index and the spatial coordinate characteristics is established based on a large number of samples, which is used to describe the sensitivity of different regions to frequency variation. When the stability index is calculated, the corresponding frequency adjustment threshold is predicted through the function mapping, so as to realize dynamic adjustment and optimization of the stable state of the sound field. The optimization point is locked based on the difference between the frequency adjustment threshold and the stability index; the region with the minimum difference is determined as the optimization point, which is a spatial position that is easy to improve the pattern stability through frequency adjustment.

[0046] Starting from the optimization point, the continuous region of the nodal line energy distribution is searched along the intensity gradient direction of the adjacent region, so as to determine the extendable range of the nodal line and obtain the nodal line expansion domain. The relief distribution is obtained through the attenuation ratio of the nodal line expansion domain and the phase conflict intensity. Specifically, the decay rate of the current phase conflict intensity with the spatial expansion distance is calculated in the nodal line expansion domain, and then the decay rate field is converted into the relief distribution. The relief distribution reflects the degree of weakening of the phase inconsistency in space after local optimization of frequency and energy, and is used to guide frequency correction and pattern stabilization control.

[0047] In the embodiment, by extracting the coordinates of the overlapping region in the nodal line distribution pattern, the nodal lines of each frequency layer are projected onto the same coordinate plane and the number of coverages is counted, and the phase conflict intensity is obtained through normalization. The spatial interference energy of the phase superposition of different frequency layers can be accurately quantified, and the problem of difficult quantification of phase conflict in multi-frequency superposition is solved, providing a reliable index for sound field stability evaluation.

[0048] When the phase conflict intensity exceeds the standing wave stability threshold, the nodal ambiguity value is calculated by combining the gray scale gradient analysis, which can intuitively reflect the attenuation degree of the nodal boundary definition. By introducing the reference state gray scale gradient and the normalized phase conflict intensity for correction, the accuracy of the ambiguity evaluation is improved, and the one-sidedness of single parameter judgment is avoided.

[0049] The phase shift distribution is obtained from the boundary gray scale gradient variation range, and the stability index is obtained through the integral ratio of the phase shift distribution and the nodal ambiguity value, which can comprehensively measure the overall relationship between the phase variation and the ambiguity of the overlapping region, realize the quantitative evaluation of the stable state of the sound field, and provide a clear basis for subsequent frequency adjustment.

[0050] Based on the sample, the stability index and the function mapping of the spatial coordinate characteristics are established, the optimization point is locked by the difference value of the frequency adjustment threshold and the stability index, the area which can be improved in stability by frequency adjustment is accurately positioned, the wave node line expansion domain is identified by combining the optimization point expansion, and the phase conflict relief distribution is obtained, the directional regulation of frequency correction and pattern stabilization is realized, the closed loop of "conflict detection-quantitative evaluation-accurate optimization" is formed, and the stability and controllability of the meditation disc audio response under the multi-frequency sound field superposition are significantly improved.

[0051] In a preferred embodiment of the present application, S4: extracting boundary points from the composite Kranii figure, determining the spacing value of adjacent boundary points according to the boundary points, and evaluating the boundary ambiguity through the spacing value of the boundary points; The boundary points are extracted from the composite Kranii figure, the spacing value is obtained by calculating the Euclidean distance between adjacent boundary points, and the wave node morphology identification value is obtained by combining the boundary gradient analysis. The boundary points refer to the positions of the gray scale gradient of a single wave node line reaching a local extreme value in the composite Kranii figure. The Euclidean distance between adjacent boundary points is calculated to obtain the spacing distribution of each boundary segment.

[0052] The gradient vector of the gray scale change is calculated in the neighborhood of each boundary point, and the module length thereof is taken to represent the intensity of the gray scale change. Then, the spacing value of the boundary points is compared with the module length of the corresponding gradient vector to form a wave node morphology identification value, which is used to reflect the comprehensive characteristics of the local geometric morphology of the wave node line and the boundary clarity. The greater the ratio is, the greater the boundary point spacing and the smaller the gray scale gradient are, indicating that the wave node morphology is fuzzy. The weighted conflict intensity in the boundary point position is calculated, and the wave node morphology identification value is taken as the weight to obtain a stability mapping through regional integration, which is used to quantify the overall structural stability of the wave node line under multi-frequency interference. wherein, is the stability mapping, is the region surrounded by all the boundary points and occupied, is the maximum weighted conflict intensity; is the local stability mapping; The weighted conflict intensity of each position is calculated by performing superposition counting by detecting the number of overlapping or adjacent points in the boundary point position, that is, the degree of coincidence of the wave node lines of multiple frequency layers in the same area. The weighted conflict intensity judgment reflects the local influence of the boundary structure instability caused by multi-frequency interference, which is specifically: , is the weighted conflict intensity, is the total number of frequency layers; is the frequency layer number, is the frequency layer is the wave node morphology identification value at the point, is a function indicating whether there is a boundary point at the coordinate, taking 1 if there is a boundary point, otherwise 0; If the stability map exceeds the preset threshold, the boundary ambiguity is obtained by the difference between the stability map and the frequency adjustment threshold, wherein the difference is calculated by subtracting the frequency adjustment threshold from the stability map; if the difference is large, it indicates that the system stability deviates from the frequency adjustment balance zone, and the frequency component needs to be re-optimized; if the difference is small, it indicates that the boundary ambiguity is in an acceptable range, and the standing wave pattern maintains stability. The difference evaluation mechanism ensures the real-time coupling regulation between the pattern boundary clarity and the frequency parameters of the sound field, thereby realizing the adaptive optimization of the wave node pattern and the overall pattern stability.

[0053] In this embodiment, the boundary points of the gray scale gradient reaching local extreme values are extracted from the composite Kranich pattern, the interval value is obtained by calculating the Euclidean distance between adjacent boundary points, and the wave node pattern recognition value is calculated by combining the length of the boundary point neighborhood gray scale gradient vector module. It can comprehensively reflect the local geometric shape of the wave node line and the boundary clarity, solve the problem that a single parameter cannot comprehensively evaluate the wave node pattern, and provide accurate local feature basis for subsequent stability analysis.

[0054] By detecting the position overlap of the boundary points or the number of adjacent points and superimposing the count, the weighted conflict strength is calculated by combining the total frequency layer and the wave node pattern recognition value. The local influence of multi-frequency interference on the stability of the boundary structure can be quantified, avoiding the defect that it is difficult to locate the unstable factors of the boundary when the multi-frequency is superimposed, and improving the accuracy of the identification of unstable areas.

[0055] Taking the wave node pattern recognition value as the weight, the stability map is obtained by area integration, which can quantitatively evaluate the structural stability of the wave node line under multi-frequency interference, realize the comprehensive evaluation from local features to overall state, solve the problem of only focusing on local features and ignoring overall stability, and provide comprehensive support for the judgment of boundary ambiguity.

[0056] When the stability map exceeds the preset threshold, the boundary ambiguity is obtained by the difference between the stability map and the frequency adjustment threshold, and whether the frequency component needs to be optimized is judged according to the difference. A real-time coupling regulation mechanism of the pattern boundary clarity and the frequency parameters of the sound field is constructed, realizing the adaptive optimization of the wave node pattern and the overall pattern stability, forming a complete link of "local feature extraction-overall stability evaluation-dynamic regulation and optimization", and significantly improving the stability and clarity of the Kranich pattern in the meditation disc audio response monitoring.

[0057] The boundary ambiguity describes the degradation of the clarity of the wave node line after being affected by phase interference, uneven energy distribution, material properties, etc. under the condition of multi-frequency sound field superposition.

[0058] In a preferred embodiment of the present application, S5: the accuracy of pattern recognition under different frequency combinations is evaluated by the boundary ambiguity and the number of frequency overlapping layers, and the dynamic change curve of distinguishability and the range of frequency layer number that can be safely adjusted are determined according to the change relationship of the accuracy with the number of frequency overlapping layers; The composite Kranii pattern under different frequency combinations is constructed, and the recognition results of each combination are counted by using the pattern recognition model to obtain the accuracy of pattern recognition corresponding to each frequency combination. Based on the accuracy, the dynamic change curve of distinguishability is generated, and the peak points in the curve are extracted. The change relationship between the accuracy and the number of overlapping layers is established by sorting and fitting the data of the change of the recognition accuracy with the number of overlapping layers, and the dynamic change curve of distinguishability is generated accordingly.

[0059] According to the peak points, the interval value of adjacent frequency combinations is determined, and the ratio of the interval value to the boundary ambiguity is calculated. Based on the ratio, the stability threshold is determined to limit the range of frequency layer number that can be safely adjusted by the system and to update the dynamic change curve of distinguishability, so as to realize the dynamic optimization and adaptive adjustment of the recognition performance.

[0060] The stability threshold is established by the ratio of the interval value of adjacent frequency combinations to the boundary ambiguity. The stability ratio curve is constructed with the number of frequency layers as the variable, and the corresponding ratio is determined as the stability threshold when the first-order change rate of the ratio curve exceeds the preset smoothing threshold, which is used to indicate the critical state of the system from the stable recognition area to the sensitive interference area.

[0061] The system extracts the peak point position where the recognition accuracy reaches the maximum value in the curve, which represents the optimal frequency combination. Then, the interval value between adjacent frequency combinations is determined with the peak point as the center, and the ratio of the interval value to the boundary ambiguity is calculated to evaluate the sensitivity of the system to frequency disturbance. According to the ratio result, the system determines the stability threshold of pattern recognition, and determines the allowed adjustment range of overlapping layers under the threshold condition, thereby limiting the frequency layer number fine tuning interval that can be performed under the condition of ensuring recognition stability. Finally, the system re-evaluates and corrects the dynamic change curve of distinguishability of different frequency combinations according to the adjustment range, so as to realize the dynamic optimization and adaptive adjustment of the recognition performance.

[0062] The system first selects several different frequency combinations, each combination corresponding to different frequency superposition layers (for example, two layers of superposition, three layers of superposition, etc.). For each frequency combination, the system inputs the driving signal into the piezoelectric driver and collects the generated composite Kranii pattern. Then, using the trained pattern recognition model (for example, a recognition network based on feature matching or deep convolution), each pattern is identified and judged. The system calculates the correct recognition times output by the model and the total number of test samples, that is, the pattern recognition accuracy under the frequency combination. This process is repeated several times to ensure statistical stability and form a frequency combination and recognition accuracy correspondence table.

[0063] In this embodiment, by constructing composite Kranii patterns under different frequency combinations and using pattern recognition models to calculate the recognition accuracy of each combination, the corresponding relationship between different frequency combinations and pattern recognition performance can be accurately obtained, solving the problem that traditional methods cannot quantify the influence of frequency combination on pattern distinguishability, and providing reliable data support for subsequent curve generation.

[0064] Based on the change relationship between recognition accuracy and frequency overlap layer number, a distinguishability dynamic change curve is generated, and the peak point of the curve is extracted to quickly locate the optimal frequency combination, avoiding the inefficient problem of blind trial of frequency combination, and improving the pertinence and efficiency of frequency combination optimization.

[0065] The peak point is used as the center to determine the interval value between adjacent frequency combinations, the ratio of which to the boundary ambiguity is calculated, and the stability threshold is determined based on the first order change rate of the ratio curve, which can accurately divide the system stable recognition area and sensitive interference area, solving the problem of lack of safety range guidance for frequency layer adjustment, and providing clear boundaries for frequency adjustment.

[0066] According to the stability threshold, the frequency layer range that can be safely adjusted is limited, and the distinguishability dynamic change curve is updated accordingly, forming a closed-loop optimization link of "frequency combination test-accuracy rate statistics-curve generation-threshold determination-range limitation-curve update", realizing dynamic optimization and adaptive adjustment of pattern recognition performance, and significantly improving the scientificity of frequency combination selection and the stability of pattern recognition in meditation plate audio response monitoring.

[0067] In a preferred embodiment of the present application, S6: obtain the heart rate stability corresponding to the alpha wave frequency band of the user, determine the curve change rate of the frequency overlap layer number according to the recognition accuracy of the distinguishability dynamic change curve, adjust the frequency number and frequency value in combination with the boundary ambiguity, optimize the pattern level distinguishability, and determine the driving frequency configuration matrix of the ceramic vibration disc according to the adjusted frequency number and frequency value. After modifying the LED color temperature switching rate, the updated driving frequency configuration matrix and integrated control parameters are obtained.

[0068] The heart rate stability corresponding to the alpha wave frequency band of the user is acquired, and the curve change rate of the recognition accuracy to the frequency superposition layer number is determined in combination with the dynamic change curve of the distinguishability. , is the curve change rate, represents the average response rate of the recognition accuracy change with the frequency superposition layer number, M is the total number of frequency combinations, is the frequency combination index, is the derivative of the recognition accuracy to the frequency layer number, is the frequency superposition layer number, is the frequency superposition layer number, is the heart rate stability, as a weighting coefficient, reflects the inhibition effect of the user's physiological state on the recognition performance fluctuation, the higher the value, the more stable the state, and the smaller the influence on the curve change rate. is the recognition accuracy under the frequency superposition layer number, is the recognition accuracy under the frequency superposition layer number, is the small change amount of the recognition accuracy at the layer number point, is the small change amount of the recognition accuracy at the layer number point, is the difference between the adjacent two layer numbers, Alpha wave refers to the frequency range of 8-13 Hz in the electroencephalogram signal, representing the state of the person being relaxed and focused. The system extracts the power spectral density of this frequency band through the electroencephalogram acquisition module (such as EEG headband) to reflect the neural stability of the user.

[0069] Heart rate stability (HRS) reflects the stability of individual physiological rhythm, and is used to measure the influence of external sound stimulation on human stability, , is the heart rate standard deviation, is the heart rate mean, According to the curve change rate and the boundary ambiguity, the frequency number and frequency value are adjusted, and the pattern hierarchical distinction clarity is obtained; , is the pattern hierarchical distinction clarity, used to reflect the distinguishable degree of each layer of the pattern after frequency optimization, is the boundary ambiguity, The higher the value, the clearer the pattern layering and the more distinguishable the structure, the lower, indicating that the multi-frequency interference causes the hierarchy to be confused, Specifically, the curve change rate is divided by the boundary ambiguity to obtain an adjustment coefficient, which reflects the sensitivity of the system to frequency changes under the condition of boundary ambiguity, and the frequency number and frequency value are adjusted according to the adjustment coefficient: , wherein, and are the adjusted frequency number and frequency value, respectively, and respectively the number of frequencies and frequency values before adjustment, and is an empirical adjustment coefficient, is an adjustment coefficient; the greater, the more obvious the blurring effect, then reduce the frequency layer or fine-tune the main frequency, the smaller, then you can increase the number of layers to enhance the structural resolution.

[0070] Based on the pattern level distinction clarity, analyze the overall recognition dynamic stability under the interference of multi-frequency sound waves, obtain stability, if the stability is lower than the preset stability threshold, it means that the pattern is significantly disturbed, then extract additional features from the alpha wave frequency band to optimize the frequency overlap layer, the additional features include peak value and duration, if it is not lower than the discrimination threshold, it means that the pattern structure is clear and the recognition result is stable, no adjustment is needed; The additional features refer to a set of key parameters extracted from the alpha wave frequency band (8-13 Hz) of the user's electroencephalogram, which can reflect the stability of physiological rhythm and neural synchrony; The calculation formula of stability is: , is the stability, which represents the overall recognition dynamic stability index under the interference of multi-frequency sound waves, which is used to quantify the index of the degree of recognition dynamic balance of the system under the condition of multi-frequency sound wave superposition, n is the current frequency superposition layer, is the derivative of accuracy rate change; The stability threshold takes the average value of the historical stability; The peak value represents the energy intensity of the electroencephalogram activity, which is used for the synchronization correction of the output amplitude of the sound wave; The duration represents the time length of rhythm maintenance, which is used for the correction of the continuous stable interval of the multi-frequency superposition layer; By introducing the alpha wave features, the system can adaptively adjust the frequency superposition layer according to the user's physiological rhythm, so that the acoustic interference mode matches the electroencephalogram rhythm; realize the closed-loop self-correction of "person-sound-pattern", so that the Kranii pattern is more stable and identifiable in the meditation state; avoid pattern drift, wave node disorder and other problems caused by too many or too few frequency configurations.

[0071] Based on the additional features and heart rate stability, update the interval value of the adjacent frequency combination, and determine the change amount of the curve change rate based on the interval value of the adjacent frequency combination after updating, based on the numerical value of the change amount of the curve change rate, judge the improvement of the identifiable degree; The interval value formula of the adjacent frequency combination after updating is: wherein is the interval value of the adjacent frequency combination after updating, is the interval value of the adjacent frequency combination before updating, is the adjustment weight, which controls the influence intensity of physiological feedback on the system; is the result of weighted summation of alpha wave peak value, duration and heart rate stability; The original frequency interval value determined by identifying the peak point is corrected by alpha wave peak value, duration and heart rate stability, so that the system dynamically adjusts the spacing between the multi-frequency superimposed layers when the individual physiological state fluctuates, thereby maintaining the structural stability and recognizability of the Kranidiotis figure and improving the adaptability of the acoustic mode to physiological rhythms.

[0072] The calculation formula of the change amount of the curve change rate is: , is the change amount of the curve change rate, and respectively represent the curve change rate obtained under the updated adjacent frequency interval and the original adjacent frequency interval before updating; if exceeds 0, it means that the adjusted frequency combination improves the recognition sensitivity, and the system records that the recognizability is improved, if does not exceed 0, it means that the interference increases, and the number of frequency layers needs to be reduced; Based on the change amount of the curve change rate, the updated pattern level distinction clarity is obtained, if the updated pattern level distinction clarity exceeds the preset threshold, it means that the optimization is effective, and the final pattern level distinction clarity is output, otherwise, the next round of frequency adjustment is entered.

[0073] Based on the adjusted frequency number and frequency value, the drive frequency configuration matrix and wave node line distribution change of the ceramic vibration disc are formed, and the preliminary curve change rate is obtained according to the wave node line distribution change; Specifically, the frequency number is mapped to the drive channel number, the frequency value is converted to the voltage excitation frequency of each channel, and each drive phase and power amplitude is allocated according to the frequency distribution function, thereby forming the drive frequency configuration matrix , is the excitation frequency (i.e. frequency value) of drive channel q, is the drive amplitude (voltage amplitude) of drive channel q, is the phase offset angle of drive channel q; q is the drive channel number; The frequency distribution function is a rule for describing how the system allocates each frequency signal on different drive channels, and its purpose is to achieve uniform coverage of wave field energy and phase stability under multi-frequency excitation conditions, which is obtained by a Gaussian type frequency distribution function; and are the adjusted frequency number and frequency value, respectively, The initial driving configuration is a ceramic vibration disc excitation parameter set generated based on the optimized frequency number and frequency value, used to define the frequency, amplitude and phase distribution of each driving unit, as a reference model of acoustic excitation, providing a basic driving condition for subsequent wave node line change rate calculation and optical projection area identification.

[0074] By weighting and fusing the preliminary curve change rate and the LED color temperature switching rate, and through gradient change analysis, the material distribution characteristic function of the vibration disc surface is obtained, and the LED color temperature switching rate is corrected through the material distribution characteristic function to obtain the optimized color temperature switching rate; thereby realizing the collaborative and balanced adjustment of the ceramic vibration disc acoustic driving and the LED optical control.

[0075] The wave node line distribution change refers to the offset amount set of the spatial position, density and morphology of each wave node line in the composite clairaut figure after adjusting the driving frequency number and frequency value; The material distribution characteristic function is obtained by , The material distribution characteristic function reflects the material response difference of the local area of the disc surface, and is used to analyze the uniformity of the illumination boundary, The preliminary curve change rate and the LED color temperature switching rate are weighted and fused, that is, the acousto-optic comprehensive response value, The spatial coordinates are The second-order partial derivative represents the change gradient of the acousto-optic response in the two-dimensional space, that is, the response difference degree of different areas of the disc surface; The optimized color temperature switching rate is obtained by , The optimized color temperature switching rate is The original color temperature switching rate is The adjustment coefficient is a proportional factor for controlling the correction amplitude, The material distribution variance is obtained by , reflecting the uniformity of the disc surface material response (the greater the variance, the more uneven); The optimized color temperature switching rate is used to extract the correction coordinate set from the overlapping area to update the driving frequency configuration matrix, and to obtain enhanced light field stability; , wherein, The enhanced light field stability is used to judge whether the LED illumination area and the ceramic vibration disc sound field reach a synchronous stable state, the greater the value, the more stable the illumination and the smoother the boundary, e is the total number of illumination sampling points, which are distributed in the LED illumination area; r is the illumination sampling point number, The light intensity deviation is the difference between the illumination sampling point brightness and the average light intensity at the illumination sampling point r; The average light intensity is is the acousto-optic consistency coefficient, used to measure the contribution weight of the matching between the light field and the sound field, is the material feature mean value, representing the average response of the disc surface; illumination uniformity correction term is used to measure the time domain stability of the LED light projection area, that is, whether the brightness changes uniformly, whether there is flickering or uneven light and dark phenomenon. The closer the result is to 1, the more uniform the illumination is, and the higher the stability is.

[0076] material uniformity weighting term is used to measure the spatial response consistency of the ceramic vibration disc surface material. If the disc surface material thickness, density or acoustic impedance difference is small, is lower, at this time the value of this term is higher, indicating that the disc surface responds more uniformly to the illumination, thereby improving the spatial stability of the overall light field. is the material non-uniformity ratio; The modified coordinate set is extracted from the overlapping area to redistribute the phase offset angle and the driving amplitude: , wherein, and are the redistributed phase offset angle and the driving amplitude, is the phase offset compensation amount at the modified coordinate set , the spatial phase correction value derived from the newly added overlapping coordinate set, is used to offset the phase desynchronization problem caused by the displacement of the nodal line, is the photoacoustic coupling correction coefficient, used to control the proportional coefficient of the material response correction amplitude, determined by experiment or calibration, is the value of the material distribution characteristic function at ; Extracting the modified coordinate set from the nodal line overlapping area means that after the system is adjusted in frequency and optimized in color temperature, a new nodal line overlapping area is calculated, and a set of overlapping point coordinates that newly appear or have a significant shift in position relative to the last time (or the initial driving state) is extracted.

[0077] The ratio of the enhanced light field stability to the preliminary curve change rate is calculated to obtain the material separation efficiency index, and according to the material separation efficiency index value, the final color temperature switching rate is determined; The material separation efficiency index is used to reflect the energy distribution coordination degree of the acousto-optic coupling system under different driving states; The preliminary curve change rate is used to quantify the spatial response gradient of the ceramic vibration disc under frequency excitation; The final color temperature switching rate is combined with the updated driving frequency configuration matrix to obtain integrated control parameters of the LED light and the ceramic vibration disc.

[0078] If the material separation efficiency index exceeds the preset efficiency threshold (based on the average stable energy of the experiment), the final color temperature switching rate is output, wherein the final color temperature switching rate is the product of the optimized color temperature switching rate and the material separation efficiency index; In the embodiment, the user's alpha wave frequency band and heart rate stability are obtained, the curve change rate is determined in combination with the recognizable dynamic change curve, the user's physiological state is included in the frequency adjustment basis, the problem that the traditional method ignores the human physiological feedback is solved, the frequency optimization is more suitable for the user's meditation state, and the adaptability of the system to the human state is improved.

[0079] The frequency quantity and the frequency value are adjusted according to the curve change rate and the boundary ambiguity, the pattern level distinction clarity is obtained, the frequency parameter is dynamically optimized through the adjustment coefficient, the level confusion caused by multi-frequency interference is avoided, the problem that the frequency configuration and the pattern clarity matching are insufficient is solved, and the pattern layering distinguishability is enhanced.

[0080] The alpha wave additional feature is introduced to optimize the frequency overlap layer, the adjacent frequency combination interval value is updated in combination with the heart rate stability, the recognizable degree improvement is judged through the curve change rate change amount, the human-sound-pattern closed loop self-correction is realized, the pattern drift and the wave node disorder are avoided, and the adaptability of the system to the user's physiological rhythm change is improved.

[0081] The driving frequency configuration matrix is formed based on the adjusted frequency parameter, the LED color temperature switching rate is corrected in combination with the material distribution characteristic function, the driving frequency configuration matrix is updated and integrated control parameters are obtained, sound and light are cooperatively adjusted, the light field stability and the material response consistency are enhanced, the standing wave pattern and the light distribution are ensured to be synchronous and clear, and the overall stability and the visualization effect of the meditation disc sound frequency response monitoring are improved.

[0082] Each threshold value involved in the present application can be obtained by the mean standard deviation method; Although the embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the audio response of a meditation disc based on Cranny graphics, characterized in that, include: S1: Obtain the acoustic signal source on the surface of the ceramic vibrating disk, extract the initial frequency components after Fourier transform, decompose and fuse its spectrum to obtain the frequency value, amplitude value and frequency quantity, and obtain the optimized frequency component set after filtering and optimization. S2: Based on the optimized frequency component set, the distribution shape of the nodal lines of the composite Krani pattern is obtained to determine the number of frequency overlap layers. The vibration intensity detection is then integrated to obtain the optimized pattern layer frequency overlap layer. S3: Extract the coordinates of the overlapping area from the distribution pattern of the wave nodes, and evaluate the phase conflict intensity of the wave overlapping area by combining the number of frequency overlap layers. When the phase conflict intensity exceeds the preset standing wave stability critical value, obtain the wave node ambiguity value through gray-scale gradient analysis. S4: Extract boundary points from the composite Kranny graphic, determine the spacing between adjacent boundary points based on the boundary points, and evaluate the boundary ambiguity through the spacing values ​​of the boundary points; S5: Evaluate the accuracy of pattern recognition under different frequency combinations by assessing the boundary ambiguity and the number of frequency overlap layers. Determine the dynamic change curve of recognizability and the range of frequency layers that can be safely adjusted based on the relationship between accuracy and the number of frequency overlap layers. S6: Obtain the heart rate stability corresponding to the user's alpha wave frequency band, identify the curve change rate of the accuracy to the number of frequency overlap layers based on the dynamic change curve of recognizability, and adjust the number and value of frequencies in combination with the boundary ambiguity to optimize the clarity of pattern layer distinction. Determine the driving frequency configuration matrix of the ceramic vibratory plate based on the adjusted number and value of frequencies. After correcting the LED color temperature switching rate, obtain the updated driving frequency configuration matrix and integrated control parameters.

2. The method for monitoring the audio response of a meditation disc based on a Kroni graphic, as described in claim 1, is characterized in that: S1 includes: Acoustic wave signals from the surface of a ceramic vibratory disk are obtained by a piezoelectric vibration sensor. After Fourier transform, the original spectrum signal is obtained, and the initial frequency components are extracted from the original spectrum signal. After spectral decomposition and fusion of the initial frequency components, the frequency and amplitude values ​​of each frequency component in the initial frequency components are obtained to determine the number of frequencies. If the number of frequencies exceeds a preset threshold, bandpass filtering and amplitude normalization are performed on the original spectrum signal to adjust the amplitude value and obtain an optimized set of frequency components. Based on the optimized frequency component set, the energy and distribution characteristics are analyzed by integrating the acoustic wave detection model to identify abnormal frequency bands and generate a frequency component set with service attributes.

3. The method for monitoring the audio response of a meditation disc based on a Kroni graphic as described in claim 1, characterized in that: S2 include: The frequency values ​​in the optimized frequency component set are used as driving signals to be input into the ceramic vibratory plate to form a multi-standing wave mode. When multiple standing wave modes exist simultaneously, multiple distribution maps are recorded by optical sensors under different frequency excitations. The multiple distribution maps under different frequency modes are registered, normalized and brightness-weighted by image overlay algorithm to obtain a composite clainnet pattern. Based on the composite clainnet pattern, the distribution shape of the nodal lines of the composite clainnet pattern is determined. Based on the distribution pattern of nodal lines, the range of boundary gray-level gradient changes is extracted, and the composite clani pattern is divided into regions to obtain the nodal line intersection density in each region. By analyzing the correspondence between the range of boundary gray-level gradient changes and the nodal line intersection density, the frequency overlap layer of the pattern is determined. The vibration intensity detection is based on the number of overlapping frequency layers. The vibration intensity detection collects intensity data from the surface of the vibrating disk, judges and identifies the density distribution of fluorescent powder particles, and obtains a set of density distributions.

4. The method for monitoring the audio response of a meditation disc based on a Krani diagram according to claim 3, characterized in that: S2 also includes: based on the density distribution set, comparing the differences in spectral peak values ​​of particles in each region through spectral response analysis to obtain the peak position shift, and analyzing the force state between particles based on the peak position shift to obtain the interaction force value reflecting the degree of particle constraint under sound pressure. When the interaction force value exceeds the preset threshold, the frequency component set is adjusted and optimized through Fourier transform to obtain the optimized pattern layer frequency overlap layer.

5. The method for monitoring the audio response of a meditation disc based on a Cranny graphic according to claim 1, characterized in that: S3 includes: obtaining the coordinates of overlapping regions from the distribution pattern of nodal lines, projecting the distribution of nodal lines of each frequency layer onto the same coordinate plane, counting the number of times each overlapping region coordinate is simultaneously covered by different frequency layers, and normalizing the counting results to obtain the phase conflict intensity. If the phase conflict intensity exceeds the preset standing wave stability threshold, the node ambiguity value will be obtained by combining gray-scale gradient analysis. The phase offset distribution is obtained from the range of boundary gray-level gradient changes, and the stability index of the overlapping region is obtained by the integral ratio of the phase offset distribution and the node ambiguity value. Based on a large number of samples, a function mapping between stability index and spatial coordinate features is established. After the stability index is calculated, the corresponding frequency adjustment threshold is predicted through the function mapping. The optimization point is locked based on the difference between the frequency adjustment threshold and the stability index. Starting from the optimization point, the continuous region of the search for nodal line energy distribution is extended along the intensity gradient direction of adjacent regions to determine the optimizable extension range of the nodal line, thus obtaining the nodal line extension domain. The distribution of mitigation is obtained by the attenuation ratio between the nodal line extension domain and the phase conflict intensity.

6. The method for monitoring the audio response of a meditation disc based on a Krani diagram according to claim 5, characterized in that: S4 includes: Boundary points are extracted from the composite Kranny pattern. The spacing value is obtained by calculating the Euclidean distance between adjacent boundary points. Combined with boundary gradient analysis, the node morphology identification value is obtained. Calculate the weighted conflict intensity at the boundary point locations, and use the node morphology identification value as the weight to obtain the stability mapping through regional integration; If the stability mapping exceeds the preset threshold, the boundary ambiguity is obtained by the difference between the stability mapping and the frequency adjustment threshold.

7. The method for monitoring the audio response of a meditation disc based on a Kroni graphic, as described in claim 1, is characterized in that: S5 includes: Composite Clani patterns under different frequency combinations are constructed, and the recognition results of each combination are statistically analyzed using a pattern recognition model to obtain the accuracy of pattern recognition for each frequency combination. Based on the accuracy, a dynamic change curve of recognizability is generated, and the peak points in the curve are extracted. Based on the peak point, the interval value of adjacent frequency combinations is determined, and the ratio of the interval value to the boundary ambiguity is calculated. Based on this ratio, a stability threshold is determined to limit the range of frequency layers that the system can safely adjust and to update the dynamic change curve of identifiability.

8. The method for monitoring the audio response of a meditation disc based on a Kroni graphic as described in claim 1, characterized in that: S6 includes: Obtain the heart rate stability corresponding to the user's alpha wave frequency band, and combine it with the dynamic change curve of recognizability to determine the curve change rate of recognition accuracy with the number of frequency overlap layers; Based on the curve change rate and boundary ambiguity, adjust the frequency quantity and frequency value to obtain the pattern layer distinction clarity; Based on the clarity of pattern hierarchy, the overall dynamic stability of recognition under multi-frequency acoustic interference is analyzed to obtain stability. If the stability is lower than the preset stability threshold, additional features are extracted from the α wave band to optimize the frequency overlap layer. The additional features include peak value and duration. Based on additional features and heart rate stability, the interval values ​​of adjacent frequency combinations are updated, and the change in the curve rate of change is determined by the updated interval values ​​of adjacent frequency combinations. Based on the change in the curve rate of change, the improvement in recognizability is judged. Based on the change in the rate of change of the curve, the pattern layer distinction sharpness is updated. If the updated pattern layer distinction sharpness exceeds the preset threshold, it indicates that the optimization is effective, and the final pattern layer distinction sharpness is output.

9. The method for monitoring the audio response of a meditation disc based on a Krani graphic, as described in claim 8, is characterized in that: S6 also includes: forming the driving frequency configuration matrix and wavelet distribution change of the ceramic vibratory plate based on the adjusted number of frequencies and frequency values, and obtaining the preliminary curve change rate based on the wavelet distribution change; By weighted fusion and gradient change analysis of the initial curve change rate and LED color temperature switching rate, the material distribution characteristic function of the vibrating disk surface is obtained. The LED color temperature switching rate is then corrected using the material distribution characteristic function to obtain the optimized color temperature switching rate. By utilizing the optimized color temperature switching rate, a corrected coordinate set is extracted from the overlapping region to update the driving frequency configuration matrix and obtain enhanced optical field stability. The ratio of enhanced light field stability to the initial curve change rate is calculated to obtain the material separation efficiency index, and the final color temperature switching rate is determined based on the material separation efficiency index value. By combining the final color temperature switching rate with the updated drive frequency configuration matrix, the integrated control parameters of the LED light and the ceramic vibratory feeder are obtained.

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