A method for intelligent analysis of musical instrument audio signals based on the Kranz graph

Abstract

The application discloses a kind of musical instrument audio signal intelligent analysis methods based on clancy figure, it is related to musical instrument manufacturing technical field, steps include: obtaining clancy figure, extracting symmetry axis angle distribution and wood texture main direction and obtaining offset angle;Wood texture anisotropic distribution area is identified based on offset angle, determine abnormal resonance energy propagation path, combine edge detection calculation panel vibration uniform area ratio, obtain resonance frequency purity percentage;With offset angle, correlation strength is constructed, input preset mapping obtains length-width modulus ratio fusion result, combined with finite element simulation to obtain panel cutting angle adjustment amount;According to adjustment amount, update modulus ratio, geometric symmetry coverage area ratio and energy dissipation space position distribution;Compare distribution before and after optimization, evaluate abnormal resonance amplitude reduction and symmetry axis angle distribution, determine the matching relationship and optimization direction of texture and symmetry axis, output final cutting angle scheme.

Description

Technical Field

[0001] This invention relates to the field of musical instrument manufacturing technology, specifically to a method for intelligent analysis of musical instrument audio signals based on Cranny graphics. Background Technology

[0002] In the field of musical instrument manufacturing, the precise control of the soundboard's vibration characteristics directly determines the quality of the sound. Kroni graphics, as an important tool for visualizing vibration patterns, can clearly show the distribution of vibration nodes on the soundboard at specific frequencies. These geometric patterns, formed by fine sand or powder on the vibrating surface, provide luthiers with an intuitive means of observing and analyzing the soundboard's resonance characteristics.

[0003] Traditional panel tuning methods often treat the symmetry of the Clani pattern and the direction of the wood grain as two relatively independent factors, ignoring the complex coupling relationship between them. When there is a misalignment between the axis of symmetry of the pattern and the main direction of the grain, or when defects are unevenly distributed, vibrational energy will deviate from the desired path and migrate along the low-impedance direction, causing abnormal energy accumulation or dissipation in local areas. This coupling effect between the axis of symmetry and the grain direction further affects the energy dissipation path in the panel, causing vibrational energy that should be evenly distributed along the axis of symmetry to actually propagate preferentially along the low-impedance direction of the wood grain, disrupting the ideal vibration mode. This results in a lag in the identification of abnormal resonance, energy accumulation, and local structural risks, making them difficult to reproduce. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent analysis method for musical instrument audio signals based on Cranny graphics, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent analysis method for musical instrument audio signals based on Cranny graphics, the method comprising: S1: Obtain the Klani graphic, extract the symmetry axis angle distribution and the main direction angle of the wood grain, and subtract them under the same reference system to obtain the offset angle; S2: Based on the offset angle, identify the anisotropic distribution area of ​​wood grain to determine the propagation path of abnormal resonance energy. Combined with the edge detection algorithm, determine the proportion of uniform vibration area of ​​the panel. By analyzing the stability of the audio vibration signal mode and the purity of the frequency, obtain the percentage of resonance frequency purity. S3: Based on the offset angle, the correlation strength is obtained. Through the set mapping function, the modulus ratio fusion result is obtained. Combined with the finite element analysis algorithm, the stress distribution is simulated to obtain the panel cutting angle adjustment amount. S4: Based on the panel cutting angle adjustment, obtain the corrected modulus ratio, the adjusted geometric symmetry coverage area ratio, and the optimized energy dissipation spatial distribution map. S5: Compare the spatial distribution of energy dissipation before and after optimization to assess the reduction rate of abnormal resonant frequency amplitude and the distribution of symmetry axis angle, identify the matching relationship between the main direction angle of wood grain and the symmetry axis, and optimize the direction to determine the final panel cutting angle scheme.

[0006] The present invention has the following beneficial effects: This invention discloses an intelligent analysis method for musical instrument audio signals based on the Kranney pattern. It aims to solve the core technical problem of misalignment between the vibration symmetry axis and the main grain direction caused by the anisotropy of wood, leading to abnormal resonance and uneven energy dissipation. This invention acquires the Kranney pattern of the instrument panel, calculates the angle difference between its symmetry axis angle and the main grain direction, and thus quantifies a key offset angle. This angle directly reflects the degree of misalignment between the panel's physical properties and the ideal vibration mode. Based on this offset angle, this invention can assess the wood's elastic modulus ratio and identify abnormal resonance energy propagation paths and dissipation spatial locations, thereby determining the purity of the resonance frequency. By establishing the correlation strength between the degree of misalignment and resonance purity, this invention can accurately calculate the panel cutting angle adjustment amount that maximizes the proportion of uniform vibration areas and predict the optimized energy dissipation distribution. Ultimately, it provides a final cutting scheme that significantly improves the symmetry of the Kranney pattern and reduces abnormal resonance, thereby achieving a precise improvement in the acoustic quality of the musical instrument. Attached Figure Description

[0007] Figure 1 This is a flowchart of an intelligent analysis method for musical instrument audio signals based on Cranny graphics, according to the present invention. Figure 2 This is a schematic diagram of an intelligent analysis method for musical instrument audio signals based on Cranny graphics according to the present invention; Figure 3 This is another schematic diagram of the intelligent analysis method for musical instrument audio signals based on Cranny graphics according to the present invention. Detailed Implementation

[0008] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0009] Example 1 Please see Figures 1 to 3This invention provides a method for intelligent analysis of musical instrument audio signals based on Cranny graphics, the method comprising: S1: Obtain the Klani graphic, extract the symmetry axis angle distribution and the main direction angle of the wood grain, and subtract them under the same reference system to obtain the offset angle; S2: Based on the offset angle, identify the anisotropic distribution area of ​​wood grain to determine the propagation path of abnormal resonance energy. Combined with the edge detection algorithm, determine the proportion of uniform vibration area of ​​the panel. By analyzing the stability of the audio vibration signal mode and the purity of the frequency, obtain the percentage of resonance frequency purity. S3: Based on the offset angle, the correlation strength is obtained. Through the set mapping function, the modulus ratio fusion result is obtained. Combined with the finite element analysis algorithm, the stress distribution is simulated to obtain the panel cutting angle adjustment amount. S4: Based on the panel cutting angle adjustment, obtain the corrected modulus ratio, the adjusted geometric symmetry coverage area ratio, and the optimized energy dissipation spatial distribution map. S5: Compare the spatial distribution of energy dissipation before and after optimization to assess the reduction rate of abnormal resonant frequency amplitude and the distribution of symmetry axis angle, identify the matching relationship between the main direction angle of wood grain and the symmetry axis, and optimize the direction to determine the final panel cutting angle scheme.

[0010] In this embodiment, the method extracts the symmetry axis angle distribution and the main direction of wood grain under the same reference system through S1 and obtains the offset angle. For the first time, it unifies the geometric symmetry information and material orientation to comparable coordinates, avoiding the deviation of previous subjective comparisons. S2 identifies anisotropic distribution regions and anomalous resonance energy propagation paths under the constraint of offset angle, and quantifies the vibration field by the proportion of uniform regions and the percentage of resonance frequency purity, turning the anomaly from "looking wrong" to "numerically identifiable".

[0011] S3 uses the correlation strength obtained from the offset angle as a bridge, combines the output modulus ratio fusion result of the mapping function, and then combines the panel cutting angle adjustment amount with the finite element stress field to form a closed-loop decision coupling material parameter identification and process orientation, which significantly reduces repeated trial cutting.

[0012] S4 dynamically corrects the established modulus ratio, geometric symmetry coverage, and energy dissipation distribution based on the angle adjustment, thereby achieving iterative correction of the effective stiffness and modes within the plate and ensuring that subsequent evaluations are based on the latest physical state.

[0013] S5 quantifies the reduction in abnormal resonance amplitude by comparing the energy dissipation distribution before and after optimization, and updates the symmetry axis angle distribution and extracts the matching relationship optimization direction accordingly, ultimately selecting a cutting scheme that meets multiple constraints.

[0014] Overall, this method organically combines the intuitive visualization of the Clani diagram with the computational feasibility of engineering. It can improve frequency purity and vibration uniformity while ensuring safety and strength, reduce the risk of local energy accumulation and resonance contamination, reduce the uncertainty and rework cost of manual adjustment, improve batch consistency and traceability, and support panel processing to move from experience-based optimization to data-driven closed-loop control.

[0015] Example 2 Please refer to Figure 1 Specifically: S1 includes: Obtain the Kranné pattern on the soundboard surface during instrument vibration, and extract the symmetry axis angle distribution and the main direction angle of wood grain from the Kranné pattern; The Chladni pattern refers to the geometric pattern formed on the surface of a panel when a standing wave is generated at a specific excitation frequency, consisting of nodal lines and antinodes. At the nodal lines, the amplitude is close to zero, making it easy for particles (such as fine sand) to accumulate, thus displaying a mesh / pattern with a certain degree of symmetry and partitioning on the surface. It reflects the mode shape characteristics and anisotropic effects of the structure at that frequency.

[0016] The process of obtaining the Kroni graphic is as follows: Preparation and securing: Secure the panel to the standard supports, ensuring that the boundary conditions can be reproduced.

[0017] Apply excitation: Gradually approach the target resonant frequency using frequency sweep or fixed frequency excitation; confirm entry into stable mode through feedback from accelerometer or exciter.

[0018] Particle spreading: Fine sand or light powder is evenly spread on the panel surface; during steady-state resonance, the particles gather at the nodal lines and are shaken off at the antinodes, gradually revealing a clear pattern.

[0019] Image acquisition: High-resolution images are acquired at a stable moment using an overhead camera to ensure uniform lighting and a flat focal plane.

[0020] The symmetry axis angle distribution is the set of angles and their intensity distribution formed by statistically analyzing the angles of all possible geometric symmetry axes (such as one or more straight lines that can make the pattern coincide as much as possible after mirroring) in a Clani graphic. It reflects how strong the mirrorability of the pattern is in different orientations.

[0021] The main direction angle of wood grain is the main direction angle formed by the dominant extension direction of wood fibers / annual rings on the panel surface relative to a reference datum (such as the long side of the panel). It reflects the natural anisotropy orientation of the material (the mechanical difference between the longitudinal and transverse grain directions).

[0022] The steps to obtain the symmetry axis angle distribution are as follows: Preprocessing: Remove shadows from the image, equalize brightness and contrast, perform background subtraction, and preserve the main structure of nodal lines.

[0023] Skeletonization / Edge Detection: Use edge detection (such as the Canny concept, but here we only describe the action: extracting the boundaries of connected lines) to obtain a clear mesh outline; refine it to find long straight lines or principal axes.

[0024] Candidate axis generation: Using the center of the panel as a reference, scan lines at different angles in segments within the range of 0° to 180° as candidate axes of symmetry; each candidate axis serves as a mirror reference.

[0025] Mirror consistency evaluation: For each candidate axis, mirror the image to the other side of the axis and calculate the overlap between the mirror image and the original image (pixel consistency ratio / error metric); the higher the overlap, the more likely that angle is to be a valid axis of symmetry.

[0026] Angular distribution formation: The mapping relationship between angle and coincidence is recorded to obtain the symmetry axis angular distribution (peak areas are formed near certain angles, representing the main symmetry orientation; if there are multiple peaks, it indicates multi-axis or approximately multi-axis symmetry).

[0027] The steps to obtain the main direction angle of wood grain are as follows: Texture visualization: Ensure consistent light oblique angle from the same or additionally captured panel texture images to highlight fiber orientation.

[0028] Local direction estimation: Divide the entire image into small regions and analyze the dominant direction of the texture direction block by block (which can be understood as finding the most obvious lines in each small region).

[0029] Main direction statistics: Summarize the main directions of small areas of the entire map, and use the mode or weighted median direction as the main direction angle of the whole board; if there is regional deviation, record the main and secondary directions and their proportions to obtain the global representative value of the main direction angle.

[0030] Angle normalization: Map the principal direction angle to the same reference system as the axis of symmetry angle (e.g., take the long side of the panel as the 0° reference) to ensure that the two can be directly subtracted.

[0031] Based on the symmetry axis angle distribution, image segmentation methods are used to divide symmetrical and asymmetrical regions to identify the geometric symmetry coverage area ratio. The geometric symmetry coverage area ratio is the proportion of the area of ​​the symmetrical region that can be covered by the pattern and its mirror image under a selected symmetry axis to the total effective area of ​​the pattern. It is used to quantify the geometric symmetry degree and modal purity trend of the modal pattern. The process of identifying the geometric symmetry coverage area ratio is as follows: Locating the principal axis of symmetry: In the distribution of symmetry axis angles, select the angle with the highest overlap as the principal axis of symmetry angle. Mirror overlap construction: Using this axis as the boundary, mirror one side of the pattern to the other side, aligning it pixel-by-pixel with the original image. Symmetry region determination: If the pattern attributes of a pixel and its mirror pixel are consistent or the error is within the allowable threshold, then the pixel is recorded as a symmetric pixel; otherwise, it is recorded as an asymmetric pixel. Area statistics: The ratio of the total area of ​​symmetric pixels to the total area of ​​the effective pattern region is the geometric symmetry coverage area ratio. A higher ratio indicates stronger mirror consistency of the pattern under this mode, indicating more regular mode shapes, more concentrated modal energy, and less influence from external disturbances or anisotropic mismatches.

[0032] Under a given candidate axis of symmetry, pixels / regions that can highly overlap with the mirror region are defined as symmetric regions; pixels / regions that do not overlap with the mirrored region or are significantly different are defined as asymmetric regions. The former reflects the modally stable structure along that axis, while the latter is mostly related to local defects, anisotropic mismatch, or boundary condition perturbations.

[0033] The principal axis of symmetry angle is determined from the distribution of symmetry axis angles. The offset angle is obtained by subtracting the principal axis of symmetry angle from the principal direction angle of the wood grain in the same angular reference system.

[0034] If the offset angle exceeds the preset threshold, the degree of misalignment between the axis of symmetry and the texture is determined, and the degree of misalignment is quantified by comparing the offset angle with the threshold.

[0035] In this embodiment, the present invention establishes a chain composed of mode shape pattern, symmetry axis angle distribution, main direction of wood grain, offset angle, and geometric symmetry coverage area ratio by standardizing the acquisition of Krahni graphics, unifying the angle reference system, and objective image analysis in S1, bringing multiple beneficial effects: Firstly, by employing fixed support, controlled excitation, grain development, and uniform overhead imaging, the reproducibility of pattern acquisition conditions is achieved, significantly reducing environmental and human variations. Secondly, the introduction of candidate axis traversal and mirror overlap evaluation forms a symmetry axis angle distribution, avoiding the subjectivity of judging symmetry based on experience and improving recognition stability and accuracy. Third, by mapping the main direction of the texture and the angle of the axis of symmetry to the same reference, the offset angle can be directly quantified and the degree of misalignment can be expressed by thresholding, so that the coupling relationship between graphics and textures can move from qualitative to quantitative. Fourth, by obtaining symmetrical and asymmetrical regions through image segmentation and calculating the ratio of geometrically symmetrical coverage area, the geometric characteristics of the vibration mode are transformed into comparable proportional indicators, which facilitates lateral comparison and longitudinal tracking between different frequency bands and different plates. Fifth, the zoning determination rules and area statistics are consistent, which can not only locate the asymmetry caused by local defects or boundary disturbances, but also provide reliable input for subsequent anisotropy identification, energy propagation deviation comparison and process perspective decision-making. Sixth, the overall process is robust to changes in lighting, noise, and details, reducing repeated trials and rework costs in the early stages of tuning, improving batch consistency and traceability, and establishing a directly reusable data foundation for purity assessment, modulus ratio identification, and angle optimization in subsequent steps.

[0036] Example 3 Please refer to Figure 1 Specifically: S2 includes: Based on the offset angle, the anisotropic distribution areas of wood grain are identified. The specific identification process is as follows: Region division: Divide the panel surface into regular grids (such as image blocks on the order of 5-10mm); The dominant direction of the texture (which can be understood as the direction in which the fiber extension is most significant) is extracted using a regular grid as a window. The direction values ​​of all pixels in the window are unified to [0°, 180°) and then summarized into a direction distribution to obtain a direction histogram. Based on the orientation histogram, the concentration and dominance of texture orientation distribution are analyzed to obtain the texture orientation consistency index; When the offset angle exceeds the preset offset threshold and the texture direction consistency index does not exceed the preset consistency threshold, the corresponding regular grid is marked as a strong anisotropic region. If the offset angle is close to the threshold and the consistency index is high, it is marked as a medium anisotropic region. If the offset angle is much lower than the threshold and the consistency index is high, it is marked as a near-isotropic region. By merging the connected domains and smoothing the boundaries of multiple sets of strong anisotropic regions, the wood texture anisotropic distribution area is obtained, which intuitively presents the regional pattern of material direction stability and geometric mode matching / mismatch. It can be directly used as the base map for subsequent abnormal energy propagation path determination and structural adjustment. Based on the anisotropic distribution of wood grain, the propagation path of anomalous resonant energy is determined. Specifically, strong anisotropic regions within the wood grain anisotropic distribution are first identified. These regions typically correspond to areas with large offset angles and unstable grain directions, indicating significant material anisotropy. When anomalous resonant frequency excitation is applied to these regions, the energy propagation path deviates, generating anomalous energy propagation paths. Using full-field response data (such as amplitude and phase fields) combined with the energy flow vector field, the energy streamlines originating from the strong anisotropic regions are traced. If energy backflow, bifurcation, or rapid attenuation is observed in these regions, they are identified as anomalous resonant energy propagation paths. Further analysis of energy accumulation points and propagation bottlenecks further determines the anomalous resonant energy propagation path, which serves as the basis for subsequent vibration control and structural adjustments.

[0037] The formula for calculating the texture orientation consistency index is: ,in The texture orientation consistency index reflects the concentration and unimodality of texture orientation distribution. The larger the value, the more consistent the orientation of each pixel, the existence of a clear dominant orientation, and the elimination of 180° symmetry ambiguity. The smaller the value, the more dispersed or multi-peaked the orientation, and the lower the consistency due to texture transitions, knots / microcracks, etc.

[0038] This is for the number of statistical samples (the number of pixels or sub-blocks within the raster used to estimate the orientation). For sample index, For the first The principal direction angle of the local texture of each sample, in radians; This is a double-angle mapping, which maps the axial angle [0,π) to the vector angle [0,2π), used to eliminate 180° directional ambiguity, so that opposite directions are treated as the same orientation when vectors are synthesized.

[0039] and For trigonometric functions, the angle is mapped to the components of a unit vector on the x (cosine) and y (sine) axes; and These are the average components of the double-angle direction vector on the x and y axes (i.e., the two coordinates of the average unit vector). Anisotropic distribution regions of wood grain refer to a set of spatial sub-regions on the surface of a panel where mechanical and vibration responses exhibit significantly different directional sensitivities. This sensitivity is reflected in directional differences such as the ratio of elastic modulus direction, shear coupling, local damping, and fiber orientation consistency. It reflects the spatial non-uniformity of the strength of anisotropy in the board and the discontinuity / turning of the principal axis. Essentially, it is how the matching of the material's intrinsic orientation and modal orientation is distributed in space.

[0040] Anomaly resonance energy propagation paths refer to the set of paths where energy flow / energy density transmission trajectories deflect, backflow, bifurcation, convergence anomalies, or sudden attenuation occur at modal resonance or near-resonance frequencies. These paths deviate significantly from the desired propagation trajectory (reference model), reflecting how factors such as anisotropic mismatch, non-ideal boundary conditions, and material defects alter energy transport and modal purity (e.g., the appearance of undesirable hot spots, fringe distortion, and standing wave node drift).

[0041] S2 also includes: By comparing the deviation between the abnormal resonance energy propagation path and the preset propagation model, the path deviation value between the two is calculated. Based on the path deviation value of the entire panel, the spatial distribution map of energy dissipation is obtained. The formula for calculating the deviation is as follows: ,in It is the path deviation value at position (x,y), reflecting the difference between the actual path and the preset path; The coordinates of the energy propagation path at position (x, y) during the actual vibration process; The coordinates of the standard energy propagation path at position (x,y) are the coordinates of the preset propagation model.

[0042] By calculating the deviation value, we can obtain a spatial distribution map of energy dissipation, that is, the degree of energy deviation at each location, and then infer which areas have abnormal energy distribution and which areas have normal energy propagation.

[0043] The preset propagation model is a theoretical or empirical model built based on historical vibration data. It is used to simulate the ideal path of energy propagation under normal conditions. This model reflects the energy propagation behavior and energy distribution characteristics of the panel under ideal conditions, including but not limited to vibration modes, resonant frequencies, amplitudes at each node, and propagation directions. The specific preset process includes: collecting historical vibration data, analyzing the panel's response at different vibration frequencies, and obtaining the standard energy propagation path. Using finite element analysis (FEA) or experimental testing, combined with the mechanical properties of the material and structural morphology, the ideal propagation model is derived. The vibration response data and theoretical model are calibrated using data fitting methods to obtain the standard propagation model.

[0044] The spatial distribution map of energy dissipation reveals which regions exhibit significant deviations in energy propagation, i.e., the location and extent of energy dissipation areas. Its core function is to reflect the differences in energy consumption and propagation efficiency across different regions. Based on the deviation between the actual path and the pre-defined model path, it uses edge detection and other algorithms to identify areas with severe energy consumption, thus revealing which regions exhibit anomalies in energy propagation. In this way, a spatial map reflecting the energy distribution status is obtained.

[0045] Based on the distribution map analysis using edge detection algorithm, the spatial coverage area is divided using the edge detection algorithm. By combining the total area of ​​the panel surface, the proportion of the panel vibration uniform area is determined. This proportion reflects whether the energy distribution is uniform during the panel vibration process. The higher the proportion of the uniform area, the more stable the vibration mode is, and vice versa, it indicates that there are non-uniform areas in the vibration.

[0046] The percentage of uniformly vibrating areas on a panel refers to the proportion of the area with uniform energy propagation to the total area. The area of ​​uniformly vibrating areas is the total area of ​​these areas obtained through an edge detection algorithm, and the total area represents the total surface area of ​​the panel. Based on the spatial distribution map of energy dissipation, edge detection algorithms (such as the Canny edge detection algorithm) are used to identify the boundaries of energy propagation paths, thereby defining the spatial coverage area. The specific steps are as follows: The input energy dissipation spatial distribution (i.e., deviation value map) is analyzed using an edge detection algorithm to identify the edges of regions with significant energy variations, i.e., the boundaries of energy propagation. Based on the connectivity of the boundary lines, energy coverage areas and energy dissipation areas are divided, with the coverage area representing regions where energy propagation is relatively uniform.

[0047] Spatial coverage is used to reflect the uniformity of energy propagation during panel vibration, i.e., which areas can maintain uniform vibration and which areas exhibit non-uniformity due to energy dissipation or large deviations.

[0048] The absolute difference between the proportion of the uniform vibration area of ​​the panel and the preset uniform threshold is determined by the proportion of the uniform vibration area, and the absolute difference is used as the initial value of the purity of the resonant frequency. The absolute difference refers to the absolute difference between the percentage of uniform vibration areas on the panel and the preset uniform threshold. The initial value of the resonant frequency purity reflects the purity of the current vibration mode, i.e., whether the panel is within the ideal resonant frequency range. If the initial value is high, it indicates that the vibration frequency deviates from the ideal state.

[0049] Based on the panel's design specifications, the standard frequency range is determined. An adjustment coefficient is obtained by dividing the initial value of the resonant frequency purity by the standard frequency range. This adjustment coefficient reflects the degree of deviation between the panel's current vibration frequency and the standard frequency range, serving as the basis for subsequent adjustments. The adjustment coefficient is then multiplied by the initial value of the resonant frequency purity to obtain the percentage of resonant frequency purity. The resonant frequency purity percentage is used to assess the stability and frequency purity of audio vibration signal modes. Specifically, the resonant frequency purity percentage reflects the stability and uniformity of the vibration frequency, while the spatial distribution map of energy dissipation and the path anomaly detection results are part of the assessment of vibration uniformity. That is to say, when energy propagation is abnormal (such as the path deviating from the expected path or severe energy dissipation), the resonant frequency purity is usually affected.

[0050] The standard frequency range refers to the ideal vibration frequency interval determined according to the panel's design specifications or a preset frequency range. It is typically set based on the panel's structural characteristics, material properties, and the expected operating environment. This frequency range reflects the resonant frequency range the panel should maintain during normal operation and is compared with the actual vibration frequency to assess whether the actual vibration behavior meets design requirements. Its function is as a reference value; it helps determine the adjustment factor for the initial purity value and ultimately affects the calculation of the purity percentage at the resonant frequency. By comparing the initial purity value with the standard frequency range, the calculated adjustment factor reflects the degree of deviation between the panel's vibration frequency and design requirements, thus guiding subsequent optimization and adjustments.

[0051] The path deviation value is used as the path anomaly detection result, and a location coordinate map is generated based on the spatial distribution map of energy dissipation. Based on the location coordinate map, a distribution location mapping is formed. The path anomaly detection result and the distribution location mapping are fused to obtain the fused deviation. If the fused deviation exceeds the preset fusion threshold, the uniform region division parameters are adjusted to obtain the purity percentage optimization value.

[0052] Uniform region division parameters refer to the standards for defining uniformly vibrating regions on the panel. These standards determine which regions should be considered vibrationally stable and which should be considered abnormal regions. When the post-fusion deviation exceeds the threshold, it indicates that the energy distribution of the panel is uneven, and these regions need to be re-divided. The adjustment method is as follows: Optimize the edge detection algorithm: Adjust the sensitivity of edge detection based on the current deviation results to ensure accurate delineation of the boundaries between uniform and abnormal regions.

[0053] Update the uniform region standard: reset the threshold and increase the sensitivity to certain vibration characteristics, so that the division standard is more in line with the current energy propagation state.

[0054] Local corrections can be made: if the deviation in certain local areas is too large, the boundary conditions or material properties of these areas can be adjusted to make their vibration modes closer to the standard.

[0055] After adjusting the uniform region division parameters as described above, the proportion of uniform vibration regions on the panel is recalculated to obtain the optimized resonant frequency purity percentage. The specific steps are as follows: Based on the updated uniform region division criteria, the proportion of uniform vibration regions on the panel is reassessed, and the optimized purity percentage is calculated. The optimized purity percentage reflects the degree of optimization of the panel's vibration frequency, indicating that the uniformity and stability of energy propagation have been improved within the current frequency range.

[0056] Distribution location mapping refers to a location coordinate map generated based on the spatial distribution of energy dissipation (i.e., the deviation values ​​at each point). This map combines the deviation values ​​with spatial locations to form a regionalized mapping of energy propagation behavior.

[0057] The formula for calculating the deviation after fusion is: ,in The deviation after fusion indicates the position. The total deviation of energy propagation; The deviation value in the distribution location mapping indicates the degree of deviation of the point from the energy dissipation region; Specifically, the deviation value in the distribution location mapping is the local anomaly intensity given for each location after aligning, normalizing, and robustly modifying the actual vibration energy field with the preset model energy field in the same plate coordinate system. The specific method is as follows: First, the actual measured amplitude, velocity, and acceleration energy indicators are spatially resampled to the same grid as the model, and amplitude normalization is performed according to the excitation power and sensor gain. Then, within the steady-state time window, a robust statistic (such as the median) is taken for the actual energy of each grid point to suppress noise. The residual is obtained by subtracting it from the expected energy of the model at that point. Then, local contrast enhancement is used to highlight spatial anomalies. For areas with low signal-to-noise ratio or insufficient coverage, a confidence mask is used to suppress them. Finally, the residual percentiles or extreme values ​​are truncated, dimensionlessly scaled, and the sign is retained. The resulting scalar is the deviation value of that location in the distribution location mapping, which is used to intuitively indicate the degree and type of anomaly in energy propagation in space.

[0058] The fused deviation is used to quantitatively characterize whether path deviation and energy dissipation anomaly exist simultaneously at the same location. The larger the value, the greater the actual harm of the abnormal resonance energy propagation at that location to the panel vibration uniformity and resonance frequency purity. Based on this, the threshold can be used to fine-tune the uniform region division parameters, recalculate the uniform region ratio, and output the purity percentage optimization value. Distribution location mapping maps the spatial distribution of energy dissipation to specific spatial locations. This allows us to visually identify areas on the panel surface where energy propagation is problematic. Through this mapping, we can more clearly pinpoint the specific locations of energy deviations.

[0059] The path anomaly detection result identifies anomalous portions of the energy propagation path by analyzing the deviation values ​​of the spatial distribution of energy dissipation. The distribution location mapping is generated based on the energy coverage area. Combining these two results and comparing the deviation values ​​between the path and the location mapping determines whether a set threshold is exceeded. If the deviation value exceeds the threshold, an anomaly is indicated, requiring further parameter adjustments. The fused result reflects the anomalous areas in the energy propagation process. Although both the energy dissipation spatial distribution map and the path anomaly detection result essentially originate from path deviation, they reflect the anomalies in panel vibration behavior from different perspectives. The distribution location mapping spatializes this reflection, projecting the energy deviation results into the actual physical space.

[0060] In this embodiment, the present invention integrates graphics, texture, energy and space into a computable process in step S2, which brings the following comprehensive benefits: First, the texture direction consistency index is calculated by using the direction histogram within the regular grid, and strong / medium / near isotropic regions are marked accordingly. After merging connected components and smoothing the boundaries, an anisotropic distribution area of ​​wood texture is formed, realizing the scale improvement from scattered pixels to stable regions. Secondly, based on the base map of the area, the propagation path of abnormal resonance energy is extracted. The energy flow direction is constructed with the full-field amplitude and phase, and anomalies such as backflow, bifurcation, and sudden drop are tracked, so that "where the problem occurs and along which line" is clearly visible. Next, the deviation field between the actual path and the preset propagation model is converted into a spatial distribution map of energy dissipation, and the spatial coverage is delineated by edge detection. Then, the proportion of uniform vibration area of ​​the panel is calculated, and the uniformity is transformed from visual judgment into a comparable proportion. Based on this, the initial value of purity is obtained by the absolute difference between the proportion and the preset uniform threshold. Then, the adjustment coefficient is generated by combining the standard frequency range and the percentage of purity at the resonant frequency is output to establish a quantitative assessment with consistent spatial and frequency caliber. Finally, the fusion path anomaly detection results are mapped to the distribution location to obtain the fused deviation. If the deviation exceeds the threshold, the uniform region division parameters are adaptively adjusted, the proportion is recalculated, and an optimized purity percentage value is obtained, forming a closed loop of diagnosis, correction, and re-evaluation. This enables the localization, quantification, and iterative suppression of anisotropic energy shifts and frequency band contamination, further improving the uniformity and resonance purity of panel vibration, and providing a traceable and reusable parameterized basis for subsequent process decisions.

[0061] Example 4 Please refer to Figure 1 Specifically: S3 includes: The offset angle is normalized to obtain the texture misalignment value. The correlation strength is obtained by comparing the purity percentage optimization value with the texture misalignment value. The correlation strength is the result of subtracting the texture misalignment value from the purity percentage optimization value; the larger it is, the greater the gap between the current purity and the expected purity that can be explained by the misalignment, that is, the stronger the anomalous coupling strength between geometric misalignment and acoustic purity (there may also be factors such as material / boundary at play).

[0062] The correlation strength and texture misalignment values ​​are used as two-dimensional inputs to a preset mapping function, and the ratio of the longitudinal elastic modulus to the transverse elastic modulus is output. If the ratio exceeds the preset ratio threshold, the ratio parameter is adjusted to obtain the modulus ratio fusion result. The modulus ratio fusion result is the final usable value of the ratio of longitudinal to transverse elastic modulus: First, the two inputs, correlation strength and texture misalignment, are converted into a preliminary modulus ratio using a calibrated mapping; then, compliance checks are performed according to the allowable range given by the material type and process. If the range is exceeded, it is shrunk or truncated to a reasonable range according to established rules; then, consistency verification and robust fusion are performed with the reference ratio of the same batch, historical stable values, and current measurement reliability, and occasional deviations and measurement fluctuations are reduced through noise reduction and smoothing; if necessary, outliers are removed and recalculated based on quality inspection results; the number obtained after completing the above boundary constraints, stabilization processing, and consistency fusion is the modulus ratio fusion result. It conforms to the physical and process boundaries and can represent the true anisotropy level of the current board, and can be directly used for subsequent rotary cutting simulation and feasibility assessment.

[0063] The ratio parameter refers to the calibrable coefficient and constraint used when mapping the correlation strength and texture misalignment values ​​to the ratio of longitudinal and transverse elastic moduli, which together determine the sensitivity and boundary of the input ratio; the modulus ratio fusion result is the final usable longitudinal and transverse modulus ratio obtained by combining the initial calculated ratio with the threshold / prior / stabilization strategy, which is used for subsequent finite element and process decisions.

[0064] The preset mapping function is obtained as follows: First, under the same material type, plate thickness, and typical process conditions, a batch of measured data of samples are collected. For each sample, two types of inputs are obtained: correlation strength and texture misalignment values. At the same time, the ratio of the longitudinal elastic modulus to the transverse elastic modulus is obtained by mechanical testing, acoustic inversion, or finite element inversion with the true modulus ratio as the target. Based on this, sample pairs are constructed. After dimensionless and robust denoising of the input, an interpretable and monotonic family of functions is selected, and physical prior constraints are applied. Cross-validation is used to minimize the prediction error, and regularization is used to suppress overfitting. Then, the baseline plate and the extreme misalignment plate are used for anchor point correction, outlier samples are removed, the uncertainty is evaluated, and finally, the parameters and version of the mapping function (including applicable material type, thickness, and frequency band description) are solidified as the runtime mapping.

[0065] Longitudinal elastic modulus refers to the linear elastic modulus when loaded along the main direction of the wood grain (fiber), reflecting the stress required per unit strain when subjected to force along the grain.

[0066] The transverse elastic modulus refers to the linear elastic modulus when loaded orthogonally to the main direction of the texture, reflecting the stress required per unit strain when the transverse texture is subjected to force.

[0067] The ratio of longitudinal elastic modulus to transverse elastic modulus is a dimensionless strength comparison index of parallel and transverse stiffness. The larger the value, the stiffer the material is parallel to the grain and the softer it is transverse to the grain. The stronger the anisotropy, the more vibration and acoustic energy tend to propagate along the grain direction, and the easier it is to generate directional modes and path deflection. The closer it is to 1, the closer it is to isotropy. During the panel rotation cutting process, based on the modulus ratio fusion result and combined with the stress distribution simulation using the finite element analysis algorithm, the cutting feasibility index is determined based on the stress distribution. The panel rotation cutting process refers to, before the actual cutting and assembly, assuming that the panel (whose material main axis and texture direction are consistent) is rotated by a candidate angle relative to the assembly / stress reference axis, and then evaluating the stress and safety of the panel in the cutting, clamping and load transfer scenarios under that angle. The specific process of simulating stress distribution using the finite element analysis algorithm is as follows: using the anisotropic stiffness determined by the modulus ratio fusion result as the material input, a two-dimensional or three-dimensional mesh consistent with the actual geometry is established on the plate surface, and the boundary and load consistent with the cutting conditions (such as clamping constraints, tool feed lateral force, out-of-plane disturbance, residual stress, etc.) are applied. The principal axes of the material are mapped to the global element according to the candidate rotation angle (i.e., the orthogonal anisotropic stiffness is rotated along this angle), and the full-field stress-strain response is solved to output the equivalent stress (such as Mises stress or a criterion equivalent stress field more suitable for wood). The formula for calculating the feasibility index of cutting is: in, As a cutting feasibility indicator, it is a quantitative measure of whether cutting is safe at a given rotation angle. Essentially, it reflects whether the maximum equivalent stress / strain within the plate is lower than the material's allowable value (i.e., safety margin) under clamping, tool feed, and process load conditions. It also reflects the degree of matching between the material's anisotropy and the toolpath and fixture constraints at that angle, its robustness to process disturbances (vibration, heat, springback), and the risk of failures such as fiber breakage, edge chipping, and micro-cracks. A value ≥1 indicates that the maximum equivalent stress does not exceed the allowable value (feasible or with sufficient safety margin), while a value <1 indicates that it may exceed the limit (infeasible or requires load reduction / angle modification / support addition). Candidate rotation angle, The allowable stress (given by quality inspection / manual / statistics) under given thickness, moisture content and defect level. For the stress-bearing areas related to the plate / toolpath, For position coordinates, The equivalent force field obtained by solving at the corresponding angle (which can be the equivalent value of the strength criterion of Mises or anisotropic correction); The stress region related to the plate / toolpath is the computational domain (search area) used to obtain the "maximum equivalent stress" in the cutting feasibility assessment. It is not an arbitrary location on the entire plate, but a spatial subdomain most relevant to the actual cutting conditions and most likely to generate peak stress. It usually includes the tool path and its left and right toolpath envelope, clamping contact area, load application area, and microcrack high-incidence zone; if necessary, an additional safety zone (such as 2-5 times the plate thickness) is added to form a conservative domain. Acquisition steps: First, import the CAD shape and thickness of the plate and the material spindle; read the actual toolpath provided by the process (the toolpath includes infeed, straight segments, corners, and retraction), and generate the toolpath envelope according to the tool diameter, depth of cut, and tool path strategy; superimpose the clamping / fixture geometry and its normal / tangential constraint patches; superimpose the load action line / surface and the possible thermal effect width; find the union of the above regions to obtain the static evaluation Ω; if you want to simulate the temporal effects of the cutting process, slide the envelope and load window along the path with the toolpath feed to form the time / position related domain Ω and solve for the stress peak step by step. Finally, Ω is projected onto the finite element mesh, and a small number of trial cuts or strain gauge points are used to verify whether the selected domain covers the actual peak position. If it does not cover the peak position, Ω is redefined by backtracking the measured peak point or by adjusting the fixture / toolpath parameters.

[0068] The load application area includes the lateral force at the infeed, the normal force, and the inertia at the acceleration / deceleration corner; the clamping contact area includes the clamping jaws, the vacuum adsorption area, and the constraint patches near the locating pins. Based on the evaluation results of the cutting feasibility index with respect to angle, a feasible angle is selected, and the angle deviation data is obtained by comparing it with the reference angle. The specific logic is as follows: first, the cutting feasibility index is calculated for each candidate angle with the rotation angle as the independent variable, and then a feasible angle is selected according to the index curve (e.g., the first time it is used). The angle that maximizes the cutting feasibility index is the angle. This feasible angle is subtracted from the current reference angle to obtain the angle deviation data. Combined with the spatial coverage, the angle deviation data is calculated as a ratio to the proportion of the panel vibration uniform area to obtain the matching coefficient. Based on the matching coefficient, the initial amount of angle adjustment is obtained. The formula for calculating the initial amount of angle adjustment is: ,in, The initial angle adjustment is a suggested angle correction after combining the feasible rotation angle given by the structural side and the vibration uniformity constraint by the acoustic side onto the same scale. Its value is obtained by combining the direction and amplitude of the angle deviation data with the uniformity background of the uniform area proportion. It is used to reflect which direction and what initial amplitude should be used to adjust the rotation cutting angle of the panel to balance structural safety and acoustic purity under the current panel and working conditions. The larger the value, the stronger the mismatch between the feasible structural angle and acoustic uniformity, and the more aggressive the initial correction needs to be.

[0069] For sign functions, indicating direction adjustment; when >0, characterized in that +1 is taken (rotated in the positive direction), when <0 is taken, characterized in that -1 (rotation in the opposite direction), when =0, characterized in that, taking, characterized in that, 0; This is angular deviation data; the magnitude indicates the angle that needs to be corrected, and the positive or negative sign indicates the direction.

[0070] This represents the percentage of the area with uniform panel vibration. The matching coefficient; The matching coefficient is used to reflect the risk intensity of angular deviation under the current vibration uniformity context. It aligns the angular recommendations given on the structural side with the uniformity constraints on the acoustic side to the same scale.

[0071] The formula for calculating angle deviation data is: ,in The data represents the angle deviation; its sign indicates the direction of adjustment, and the absolute value indicates the magnitude of correction required. To ensure that the feasibility indicators for cutting meet the requirements (such as...) Or the angle at which the maximum safety margin is reached. For existing processes or assembly reference angles; The initial angle adjustment amount is multiplied and corrected using the amplification factor corresponding to the correlation strength to obtain the panel cutting angle adjustment amount: ,in This is the adjustment amount for the panel cutting angle; The magnification factor (a calibrated value used to control the magnification amplitude; in engineering, an upper limit can be set to avoid over-correction), and its value is greater than 0. For correlation strength; The coefficients extracted for correlation strength are used to map the strength of the anomalous coupling between "geometric misalignment and purity" to the amplification / suppression of the angle adjustment amplitude. The panel cutting angle adjustment refers to the final rotational cutting angle correction value given after considering factors such as structural feasibility, acoustic purity, and geometric misalignment coupling. It is used to reflect, under the current panel and working conditions, in which direction and by what magnitude the cutting / assembly angle should be adjusted to simultaneously meet the requirements of safe cutting and uniform vibration and frequency purity.

[0072] In this embodiment, the present invention first normalizes the offset angle into a texture misalignment value and compares it with the purity percentage optimization value to obtain the correlation strength, so that the originally separated graphic misalignment and frequency domain purity are linked on a single scale, which facilitates stable threshold determination and batch comparison.

[0073] Secondly, using the correlation strength and texture misalignment values ​​as two-dimensional inputs, the data is fed into a mapping function calibrated by samples of the same material type. The longitudinal and transverse elastic modulus ratios are directly output. After multiple consistency checks of allowable range, reference value, and confidence level, the modulus ratio fusion result is obtained, which significantly reduces the influence of occasional noise and measurement fluctuations, and ensures that the material anisotropy parameters can be used for engineering decision-making.

[0074] Furthermore, the modulus ratio fusion result is introduced into the finite element solution to solve the stress distribution under the actual clamping and cutting conditions, forming a cutting feasibility index curve consistent with the process. Based on this, the feasible angle is automatically selected, avoiding the subjectivity of experience-based angle selection and the risk of rework.

[0075] Subsequently, the feasible angle is compared with the reference angle to obtain angle deviation data, and a matching coefficient is formed by combining it with the proportion of the vibration uniform region. The initial amount of angle adjustment is then output, so that structural safety and acoustic uniformity are synergistically balanced under the same scale. Finally, the initial amount is multiplied and corrected according to the amplification factor corresponding to the correlation strength to obtain the panel cutting angle adjustment amount, realizing adaptive control of "the stronger the abnormal coupling, the more sufficient the correction".

[0076] Overall, S3 transforms upstream identification results into executable angle corrections, reducing the number of trial cuts and calibration iterations, improving the repeatability, traceability, and first-time success rate of cutting orientation decisions, and providing a solid material and process foundation for subsequent optimization of geometric symmetry and energy distribution.

[0077] Example 5 Please refer to Figure 1 Specifically: S4 includes: Based on the panel cutting angle adjustment, identify the ratio of the adjusted longitudinal elastic modulus to the transverse elastic modulus, obtain the ratio correction amount through subtraction, and get the corrected modulus ratio result. The corrected modulus ratio result is the original modulus ratio result plus the ratio correction amount; The ratio correction amount refers to the ratio of the longitudinal elastic modulus to the transverse elastic modulus after angle adjustment, minus the ratio of the longitudinal elastic modulus to the transverse elastic modulus before angle adjustment; The angle adjustment essentially involves rotating the material's principal axis (with or across the grain) relative to the assembly / stress reference axis to a more suitable position. In engineering evaluation, we are concerned with the "equivalent anisotropy under the assembly reference axis system," which changes with the rotation angle: when rotated, the axis-grain offset angle decreases, and the effective anisotropic strength (i.e., longitudinal / transverse equivalent stiffness comparison) exhibited by the material under the reference axis system changes accordingly. Therefore, it is necessary to "identify" the adjusted modulus ratio based on this.

[0078] Based on the corrected modulus ratio results, the geometric symmetry coverage ratio and energy dissipation spatial distribution of the Klani graphic are re-evaluated, resulting in the adjusted geometric symmetry coverage ratio and the optimized energy dissipation spatial distribution map.

[0079] Since the ratio of longitudinal and transverse moduli (and its orientation relative to the reference axis) will rewrite the equivalent stiffness matrix and intrinsic modes of the plate, it may change the standing wave junction (Clanney figure) and the stability of the principal axis of symmetry in that frequency band; if the change is small, the modes are not reordered and the position of the axis of symmetry remains basically unchanged, the coverage area ratio can remain unchanged or approximately unchanged within the allowable error; otherwise, it should be recalculated with the corrected modulus ratio as input.

[0080] The corrected modulus ratio results change the equivalent stiffness matrix and modal characteristics of the plate in the reference axis system, which in turn affects the position of the standing wave junction, the energy flow direction and the attenuation channel. These are the basis on which the spatial distribution of energy dissipation (based on the deviation field of "actual path and preset model") depends. Therefore, it is necessary to optimize the spatial distribution of energy dissipation. S5 includes: By comparing the spatial distribution maps of energy dissipation before and after optimization, abnormal resonance frequency data is obtained, and the amplitude reduction ratio is obtained based on the abnormal resonance frequency data. Abnormal resonant frequency data refers to frequency points or bands that exhibit significant dissipation or resonance peaks before optimization and show a significant decrease after optimization. This is achieved by integrating the spatial distribution of energy dissipation before and after optimization along the frequency spectrum, resulting in two frequency curves (dissipation amplitude - frequency spectrum). The main peak (or a peak with a threshold) is identified in the curve before optimization, and the difference and proportion before and after optimization are calculated at these peaks. Any peaks with a positive difference and a proportion exceeding the corresponding threshold are classified as abnormal resonant frequency data. Abnormal resonant frequency data includes, but is not limited to, abnormal frequency identifiers, frequency points, abnormal frequency bands, amplitude differences, and changes in center frequency. The amplitude reduction ratio is the percentage decrease in dissipation amplitude after optimization relative to before optimization, reflecting the degree to which abnormal resonance / abnormal dissipation is suppressed at that frequency (the larger the value, the more sufficient the suppression). Its calculation formula is as follows: ,in, For the amplitude reduction ratio result, 1 indicates that the dissipation peak at that frequency is completely eliminated, 0 indicates no improvement, and a negative value indicates a deterioration; Before optimization at frequency Spatial average dissipation amplitude, To optimize the frequency Spatial average dissipation amplitude; For frequency, This represents the difference in the rate of decrease. Based on the amplitude reduction ratio results, the symmetry axis angle distribution is evaluated to obtain the optimized symmetry axis angle distribution. Specifically: First, the "amplitude reduction ratio results" are weighted and back-projected onto the spatial distribution of energy dissipation according to frequency. Regions that achieve a large reduction ratio at each anomalous frequency are given a higher "stabilization weight" to form an optimized full-field response weight map. Under the same plate coordinate system, using this weight map as pixel weight, a mirror consistency scan is performed on the Klani graphic again: for each candidate angle, the "weighted coincidence" (i.e., the overlap consistency of the graphic with its mirror image about that angle is obtained by weighted integration) is evaluated angle by angle in the range of 0-π, and the function curve of angle → coincidence is obtained. The curve is normalized and (optionally) slightly smoothed to obtain the optimized symmetry axis angle distribution. Its peak direction represents the main symmetry axis, the peak width reflects the concentration, and because the high reduction ratio region is strengthened, the distribution is more focused than before optimization and can better reflect the true symmetry after suppressing anomalies. The main direction of wood texture is extracted based on the optimized symmetry axis angle distribution to identify the matching relationship between the main direction of wood texture and the symmetry axis, and the distribution matching degree is obtained. If the distribution matching degree exceeds the preset matching threshold, the optimization direction is adjusted by addition operation to obtain the matching relationship optimization direction. The approach to adjusting and optimizing the direction using additive operations is as follows: First, determine the current reference direction as the baseline. Then, accumulate corrections from different sources to form a new direction suggestion. These sources typically include corrections from structural feasibility angles, vibration uniformity and energy coverage, geometric and acoustic coupling strength, and safety corrections triggered by process constraints or quality inspection results. The accumulated direction suggestion is then subject to boundary and step size limits to avoid excessive adjustments at once. Simultaneously, historical data is used for smoothing to reduce the impact of occasional noise. If the new direction after accumulation still results in unsatisfactory stress or purity, the small corrections corresponding to the errors are added to the next round, forming a progressive update that approaches the target round by round until both safety and acoustic thresholds are met.

[0081] The matching relationship optimization direction refers to the "optimal forward direction" along which the panel should be fine-tuned to better align the "main direction of wood grain" with the "Klanny graphic symmetry axis" while simultaneously improving geometric symmetry coverage and frequency purity. The method for obtaining this is as follows: Using the current installation angle as a baseline, recalculate the alignment degree (e.g., whether the angle between the main direction and the symmetry axis decreases), the more concentrated symmetry axis angle distribution, the improved geometric symmetry coverage area ratio, and the improved energy dissipation and purity under both positive and negative small rotational perturbations. Combine these improvements into a single evaluation value. Compare the comprehensive evaluations of the two perturbations; the side that improves the comprehensive evaluation is the matching relationship optimization direction. Then, perform a limited-amplitude additive update along this direction, and check if further improvement is achieved. If still effective, iterate step-by-step until the gain approaches saturation or reaches the process and safety boundaries.

[0082] The formula for calculating the distribution matching degree is: in, The distribution matching degree is as close as 1, indicating that the distribution as a whole revolves around... More concentrated; Pi; Let be a differential element with respect to angle, representing the integral over the angle domain; The optimized symmetry axis angle distribution is located at angle The value at; The main direction angle of the wood grain. The angle variable is the candidate axis of symmetry. As a normalization factor, the distribution is used as the weight for expectation; It is an axial matching kernel used to map the axial alignment degree between the candidate symmetry axis angle and the main direction of wood grain to a similarity of 0 to 1; After determining the axis of symmetry along the matching relationship optimization direction and calculating the basic geometric symmetry of the Kranny figure, and combining it with the proportion of the uniform vibration area of ​​the panel, the corrected geometric symmetry index is obtained. The basic geometric symmetry is the ratio of the area of ​​the overlapping symmetric region of the graphic and its mirror image about the selected axis of symmetry (obtained by boundary integral) to the total effective area of ​​the graphic (obtained under the same mask / threshold aperture); The revised formula for calculating the geometric symmetry index is as follows: ,in This is the corrected geometric symmetry index. Based on basic geometric symmetry, This represents the percentage of the area with uniform panel vibration. and These are the ratios of the longitudinal elastic modulus to the transverse elastic modulus, before and after angle adjustment, respectively. A reference scale for allowing variations in modulus ratio (an acceptable range of variation given by the material type / process, used for dimensionless designation). This is the modulus ratio correction amount; The geometric symmetry index is used to quantify "the degree of mirror symmetry of the current Kroni pattern around the selected axis of symmetry". It takes a value of 0-1, with values ​​closer to 1 indicating a more symmetrical pattern and more complete symmetry coverage, which better supports the purity and stability of the resonant frequency.

[0083] Based on the modified geometric symmetry index, the target and constraints are set, several candidate cutting angle schemes are generated, and each candidate cutting angle scheme is simulated using the finite element method. The angle is used as input, the scheme parameters are solved and output, and the final panel cutting angle scheme is determined.

[0084] The objectives include geometric objectives: maximizing the corrected geometric symmetry index, improving the symmetry coverage ratio, and achieving a high degree of matching between the symmetry axis and the main direction of the texture; acoustic objectives: increasing the reduction ratio of the amplitude of abnormal resonance frequencies and improving the purity of resonance frequencies; and structural objectives: minimizing stress peaks, controlling deformation, and improving cutting stability.

[0085] Constraints include material and process limitations, safety and quality thresholds, and production constraints.

[0086] Material and process constraints include allowable stress, allowable displacement, fixture interference, tool incident angle range, and minimum step angle; safety and quality thresholds include cutting feasibility index not lower than the threshold, SNR threshold, allowable modulus ratio range, and uniform region proportion not lower than the lower limit; production constraints include adjustable angle discrete set, number of tool changes, and cycle time limit. The candidate cutting angle scheme is a set of rotation angles (single angle or angle sequence) to be evaluated, generated near the "matching relationship optimization direction" and in combination with the process step and the allowable angle of the equipment.

[0087] The scheme parameters are a set of comparable indicators output from the finite element method and acoustic evaluation of each candidate angle, used for scoring and selection. These parameters include: Structural side: peak equivalent stress field, location of the danger zone, feasibility index for cutting, maximum deflection, and stability margin; Acoustic side: Concentration of symmetry axis angle distribution after optimization, geometric symmetry index, reduction ratio of abnormal resonance frequency amplitude, and purity of resonance frequency; Process side: toolpath feasibility, fixture accessibility, angle adjustment cost and cycle time impact; Comprehensive aspects: objective function value, whether all constraints are met, risk level and credibility.

[0088] The final panel cutting angle scheme is the angle (or combination of angles) that achieves the optimal (or suboptimal but more robust) overall objective while satisfying all constraints; that is, the cutting orientation that is actually issued and executed. The formation method is as follows: The finite element method was used to solve for each candidate angle and collect the parameters of the scheme; Judgment and ranking are based on objectives and constraints (primary and secondary objectives or multiple objectives can be set in parallel). Compare the robustness and execution costs of parallel or similar solutions; The solution with the highest score and lowest risk is selected and solidified as the final panel cutting angle solution for process issuance and on-site verification.

[0089] In this embodiment, the present invention constructs a linkage chain in S4-S5 consisting of angle correction, material equivalence, modal re-evaluation, energy suppression, and orientation closed loop, forming an optimized closed loop that can be directly executed for manufacturing, and has significant beneficial effects: First, based on the panel cutting angle adjustment amount, the adjusted longitudinal / transverse elastic modulus ratio is dynamically identified, and the corrected modulus ratio result is obtained iteratively with the ratio correction amount, so that the engineering characterization of material anisotropy is precisely aligned with the assembly reference axis, avoiding the accumulation of deviations caused by relying solely on the original estimate.

[0090] Secondly, using the corrected modulus ratio as input, the geometric symmetry coverage ratio and energy dissipation spatial distribution map of the Klani graph are re-evaluated. If necessary, the stability of the standing wave junction and the principal axis of symmetry are refreshed, thereby ensuring that subsequent judgments are always based on the "current true stiffness state" and improving the timeliness and reliability of the judgments.

[0091] Third, by comparing the distribution maps before and after optimization, abnormal resonance frequency data can be automatically extracted and the amplitude reduction ratio can be calculated, directly quantifying whether "abnormal peaks have been effectively suppressed". This transforms the abstract improvement of timbre into verifiable digital evidence, which is beneficial for batch quality control and traceability.

[0092] Fourth, the amplitude reduction ratio is used to perform weighted back-projection on the full field response to obtain the optimized symmetry axis angle distribution. Then, the distribution matching degree is calculated in combination with the main texture direction. After threshold determination, the matching relationship optimization direction is generated to further avoid over-correction or local optima caused by a single index.

[0093] Fifth, under the framework of objectives and constraints, candidate cutting angle schemes are generated in the direction of matching relationship optimization, and the scheme parameters and cutting feasibility indicators are obtained by finite element solution. The schemes are uniformly sorted according to structural, acoustic and process multi-dimensional indicators, and finally the final panel cutting angle scheme that meets the requirements of safety threshold, purity improvement and production cycle is given, which significantly reduces the number of trial cuts and manual repetition.

[0094] Sixth, the entire process uses a data chain that connects “corrected modulus ratio results - geometric symmetry coverage area ratio - energy dissipation spatial distribution map - abnormal resonant frequency data - amplitude reduction ratio results - distribution matching degree - cutting feasibility index - scheme parameters”. The parameters are reusable and the thresholds are calibrable, which not only improves the purity of the resonant frequency and the uniformity of vibration, but also takes into account the processing strength and process stability. At the same time, it has stronger robustness and traceability to batch differences, material fluctuations and boundary condition changes, supporting the panel tuning to move from experience-based tuning to data closed-loop control and large-scale consistent production.

[0095] All thresholds involved in this method can be obtained from the mean and standard deviation; Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent analysis of musical instrument audio signals based on Cranny graphics, characterized in that, The method includes: S1: Obtain the Klani graphic, extract the symmetry axis angle distribution and the main direction angle of the wood grain, and subtract them under the same reference system to obtain the offset angle; S2: Based on the offset angle, identify the anisotropic distribution area of ​​wood grain to determine the propagation path of abnormal resonance energy. Combined with the edge detection algorithm, determine the proportion of uniform vibration area of ​​the panel. By analyzing the stability of the instrument's audio vibration signal mode and the purity of the frequency, obtain the percentage of resonance frequency purity. S3: Based on the offset angle, the correlation strength is obtained. Through the preset mapping function, the modulus ratio fusion result is obtained. Combined with the finite element analysis algorithm, the stress distribution is simulated to obtain the panel cutting angle adjustment amount. S4: Based on the panel cutting angle adjustment, obtain the corrected modulus ratio, the adjusted geometric symmetry coverage area ratio, and the optimized energy dissipation spatial distribution map. S5: Compare the spatial distribution of energy dissipation before and after optimization to assess the reduction rate of abnormal resonant frequency amplitude and the distribution of symmetry axis angle, identify the matching relationship between the main direction angle of wood grain and the symmetry axis, and optimize the direction to determine the final panel cutting angle scheme.

2. The intelligent analysis method for musical instrument audio signals based on Cranny graphics according to claim 1, characterized in that, S1 includes: Obtain the Kranné pattern on the soundboard surface during instrument vibration, and extract the symmetry axis angle distribution and the main direction angle of wood grain from the Kranné pattern; Based on the symmetry axis angle distribution, combined with image segmentation methods, symmetric and asymmetric regions are divided to identify the ratio of geometrically symmetrical coverage area. The principal axis of symmetry angle is determined from the distribution of symmetry axis angles. The offset angle is obtained by subtracting the principal axis of symmetry angle from the main direction angle of the wood grain.

3. The intelligent analysis method for musical instrument audio signals based on Cranny graphics according to claim 1, characterized in that, S2 include: Based on the offset angle, the anisotropic distribution areas of wood grain are identified. The specific identification process is as follows: Divide the panel surface into a regular grid; The dominant direction of the texture is extracted using a regular grid as a window. The direction values ​​of all pixels within the window are unified to [0°, 180°) and then summarized into a direction distribution to obtain a direction histogram. Based on the orientation histogram, the concentration and dominance of texture orientation distribution are analyzed to obtain the texture orientation consistency index; When the offset angle exceeds the preset offset threshold and the texture direction consistency index does not exceed the preset consistency threshold, the corresponding regular grid is marked as a strong anisotropic region; by merging the connected components and smoothing the boundaries of multiple strong anisotropic regions, the wood texture anisotropic distribution region is obtained. Based on the anisotropic distribution area of ​​wood grain, the propagation path of abnormal resonance energy is determined.

4. The intelligent analysis method for musical instrument audio signals based on Cranny graphics according to claim 3, characterized in that, S2 also includes: By comparing the deviation between the abnormal resonance energy propagation path and the preset propagation model, the path deviation value between the two is calculated. Based on the path deviation value of the entire panel, the spatial distribution map of energy dissipation is obtained. Based on the distribution map analysis using edge detection algorithms, the spatial coverage area is divided using edge detection algorithms. By combining the total area of ​​the panel surface, the proportion of the panel with uniform vibration is determined. The absolute difference between the proportion of the uniform vibration area of ​​the panel and the preset uniform threshold is determined by the proportion of the uniform vibration area, and the absolute difference is used as the initial value of the purity of the resonant frequency. Based on the panel's design specifications, the standard frequency range is determined. The adjustment coefficient is obtained by dividing the initial value of the resonant frequency purity by the standard frequency range. The adjustment coefficient is used to reflect the degree of deviation between the current vibration frequency of the panel and the standard frequency range. The adjustment coefficient is then multiplied by the initial value of the resonant frequency purity to obtain the percentage of resonant frequency purity. The path deviation value is used as the path anomaly detection result, and a location coordinate map is generated based on the spatial distribution map of energy dissipation. Based on the location coordinate map, a distribution location mapping is formed. The path anomaly detection result and the distribution location mapping are fused to obtain the fused deviation. If the fused deviation exceeds the preset fusion threshold, the uniform region division parameters are adjusted to obtain the purity percentage optimization value.

5. The method for intelligent analysis of musical instrument audio signals based on Cranny graphics according to claim 4, characterized in that, S3 includes: The offset angle is normalized to obtain the texture misalignment value. The correlation strength is obtained by comparing the purity percentage optimization value with the texture misalignment value. The correlation strength and texture misalignment values ​​are used as two-dimensional inputs to a preset mapping function, and the ratio of the longitudinal elastic modulus to the transverse elastic modulus is output. If the ratio exceeds the preset ratio threshold, the ratio parameter is adjusted to obtain the modulus ratio fusion result. During the panel rotation cutting process, based on the modulus ratio fusion result and combined with the stress distribution simulation using the finite element analysis algorithm, the cutting feasibility index is determined based on the stress distribution. Based on the evaluation results of the cutting feasibility index with the angle, a feasible angle is selected and compared with the reference angle to obtain the angle deviation data; combined with the spatial coverage, the angle deviation data is calculated as a ratio with the proportion of the panel vibration uniform area to obtain the matching coefficient; based on the matching coefficient, the initial amount of angle adjustment is obtained. The initial angle adjustment amount is multiplied and corrected by the amplification factor corresponding to the correlation strength to obtain the panel cutting angle adjustment amount.

6. The intelligent analysis method for musical instrument audio signals based on Cranny graphics according to claim 1, characterized in that, S4 includes: Based on the panel cutting angle adjustment, identify the ratio of the adjusted longitudinal elastic modulus to the transverse elastic modulus, obtain the ratio correction amount through subtraction, and get the corrected modulus ratio result. Based on the corrected modulus ratio results, the geometric symmetry coverage ratio and energy dissipation spatial distribution of the Klani graphic are re-evaluated, resulting in the adjusted geometric symmetry coverage ratio and the optimized energy dissipation spatial distribution map.

7. The intelligent analysis method for musical instrument audio signals based on Cranny graphics according to claim 6, characterized in that, S5 include: By comparing the spatial distribution maps of energy dissipation before and after optimization, abnormal resonance frequency data is obtained, and the amplitude reduction ratio is obtained based on the abnormal resonance frequency data. Based on the amplitude reduction ratio, the symmetry axis angle distribution is evaluated to obtain the optimized symmetry axis angle distribution. The main direction of wood grain is extracted based on the optimized symmetry axis angle distribution to identify the matching relationship between the main direction of wood grain and the symmetry axis, and the distribution matching degree is obtained. If the distribution matching degree exceeds the preset matching threshold, the optimization direction is adjusted by addition operation to obtain the optimized matching relationship direction. The axis of symmetry is determined along the direction of matching relationship optimization, and the basic geometric symmetry of the Kroni figure is calculated. Combined with the proportion of uniform vibration area of ​​the panel, the corrected geometric symmetry index is obtained. Based on the modified geometric symmetry index, the target and constraints are set, several candidate cutting angle schemes are generated, and each candidate cutting angle scheme is simulated using the finite element method. The angle is used as input, the scheme parameters are solved and output, and the final panel cutting angle scheme is determined.

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