Production equipment state real-time analysis method and system based on edge calculation

Abstract

The invention provides a production equipment state real-time analysis method and system based on edge calculation, and relates to the technical field of information, and the method comprises the steps: analyzing the dynamic change trend of a light beam central axis according to the offset trajectory of the light beam central axis, and determining the spatial distribution characteristics of the light beam axis through the dynamic change trend of the light beam central axis in combination with an LED array arrangement SIC mode; if the adjustment angle in the lens calibration instruction set exceeds a preset angle threshold, dynamically adjusting the position of the optical axis of the lens according to the offset amplitude and direction of the optical axis, compensating the offset of the light beam axis in real time, and obtaining a calibrated optical axis parameter; according to the color temperature distribution uniformity, determining a target lens position through a lens position adjustment attribute; according to the calibrated optical axis parameters and color temperature distribution uniformity, stable light beam center axis distribution is determined, spectrum consistency is determined, and a performance evaluation data set is obtained through comprehensive evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a production equipment state real-time analysis method and system based on edge computing. BACKGROUND

[0002] The mounting of light-emitting diode (LED) light sources and the configuration of optical lenses are core links in modern lighting technology, and their performance directly determines the lighting quality and energy efficiency of lamps. With the growing demand for high-quality lighting, how to ensure the spectral consistency between LED light sources and lens configurations has become the focus of the industry. The importance of this field lies in the fact that precise optical matching not only concerns the uniformity and comfort of lighting, but also directly affects the energy efficiency and service life of lamps, especially in high-end commercial lighting, automotive lighting, and other scenarios, where spectral consistency is crucial to user experience. Existing methods often only focus on the mechanical precision of LED chip mounting or the static optical axis calibration of lenses when achieving spectral consistency. For example, the existing patent CN102854000B "High-brightness LED optical axis detection device and optical axis deviation detection method" can only perform static optical axis deviation detection and simple qualified product screening, lacking dynamic compensation mechanisms and collaborative optimization capabilities with lens configurations, and cannot solve the dynamic interaction problem of LED mounting deviation and lens optical axis calibration. This fragmented approach makes it difficult to achieve optimal overall light efficiency, especially in complex lighting scenarios, where small deviations can be amplified, affecting the stability of light distribution. The core challenge of the research lies in the contradiction between the micro-deviation of LED chip mounting position and the dynamic response of lens optical axis calibration. The mounting deviation of LED chips can cause a slight drift in the light-emitting angle of the light-emitting diode (LED) light source, which directly changes the direction of the light beam center axis, and thus causes uneven distribution of lens light. For example, in the production of automotive headlamps, if the LED chip deviates by 0.1 millimeters, it may cause the light beam center to deviate from the expected angle, resulting in local color temperature unevenness and affecting the visual clarity of night driving. Therefore, how to ensure the stability of the light beam center axis and the uniformity of color temperature distribution through dynamic adjustment of the lens optical axis calibration mechanism when the LED chip mounting position deviates slightly has become a key problem in improving the overall illuminance uniformity and glare control effect of lamps. SUMMARY

[0003] To solve the above technical problems, the present application provides a production equipment state real-time analysis method based on edge computing, mainly including: Obtaining the position information of the LED chip mounting process, obtaining the X-Y coordinate deviation and rotation angle deviation of the chip relative to the substrate in the mounting process through real-time monitoring by a machine vision system, and generating a mounting position deviation distribution data set; extract the offset angle and displacement vector of the LED chip from the mounting position deviation distribution data set, and convert the offset trajectory of the light beam center axis from the chip offset angle and displacement vector according to the chip packaging structure and the geometric characteristics of the light source; According to the offset trajectory of the light beam center axis, the dynamic change trend of the light beam center axis is analyzed, and the spatial distribution characteristics of the light beam axis are determined from the dynamic change trend of the light beam center axis combined with the SIC arrangement of the LED array. Through the spatial distribution characteristics of the light beam axis, the optical axis offset mode and the abnormal offset point are identified, the real-time adjustment angle and displacement amount of the lens optical axis are calibrated according to the identified offset mode, and the lens calibration instruction set is generated based on the adjustment angle and displacement amount. If the adjustment angle in the lens calibration instruction set exceeds the preset angle threshold, the lens optical axis position is dynamically adjusted according to the optical axis offset amplitude and direction, the light beam axis offset is compensated in real time, and the calibrated optical axis parameters are obtained. Through the calibrated optical axis parameters, the propagation path of the light in the lens is analyzed, the light propagation path is simulated and processed, the light distribution data set is generated, and the color temperature distribution uniformity is determined according to the light spectrum characteristics of the LED chip. According to the color temperature distribution uniformity, the target lens position is determined through the lens position adjustment attribute. According to the calibrated optical axis parameters and the color temperature distribution uniformity, the stable light beam center axis distribution is determined to determine the spectral consistency, and the performance evaluation data set is obtained through comprehensive evaluation.

[0004] Further, the position information of the LED chip mounting process is obtained, the X-Y coordinate offset and rotation angle offset of the chip relative to the substrate in the mounting process are obtained in real time through a machine vision system, and a mounting position deviation distribution data set is generated, including: The image acquisition of the chip position is performed through the machine vision system, and the original pixel data is generated. The edge detection algorithm is used to determine the chip edge profile according to the original pixel data, and the center coordinate value of the chip edge profile is obtained. According to the comparison between the center coordinate value and the substrate reference point, the twist difference value of the rotation angle and the vector component of the coordinate offset are calculated. The classification label is generated according to the twist difference value and the vector component. The historical sequence of angle deviation is integrated according to the classification label, and a time axis correlation matrix is constructed. The statistical characteristics of the clustering group are generated by clustering processing according to the time axis correlation matrix. The dynamic path of the mounting process is fused according to the statistical characteristics, a multi-dimensional deviation mapping is generated, and the mounting position deviation distribution data set is output.

[0005] Further, the offset angle and displacement vector of the LED chip are extracted from the mounting position deviation distribution data set, and the offset trajectory of the light beam center axis is converted from the chip offset angle and displacement vector according to the chip packaging structure and the geometric characteristics of the light source. The initial angle value of the offset angle and the displacement component of the displacement vector are extracted from the mounting position deviation distribution dataset to construct vector-angle pairs and generate a composite deviation set. The chip casing parameters of the chip package structure are analyzed based on the composite deviation set, and the curvature radius data of the light source geometry are fused to generate a structural fusion body. The package geometry is mapped through the structural fusion body to determine the initial offset path of the beam axis. The path nodes are adjusted according to the initial offset path to generate a corrected offset path. The conversion factor is processed according to the corrected offset path to generate an axial sequence. The trajectory is smoothed according to the axial sequence to generate a continuous trajectory line. The offset trajectory of the beam center axis is determined based on the continuous trajectory line and the deviation data filtering threshold.

[0006] Furthermore, the step of analyzing the dynamic change trend of the beam center axis based on the offset trajectory of the beam center axis, and determining the spatial distribution characteristics of the beam axis from the dynamic change trend of the beam center axis by combining the LED array arrangement SIC method, includes: A trend sequence is extracted from the offset trajectory of the beam center axis to construct a trend variation map; the LED array coordinates are fused according to the trend variation map to generate a layout grid; the correlation chain of the offset trend is integrated according to the layout grid to generate a fused trend body; the axial interval is quantified according to the fused trend body to determine the radial dispersion; the angular tilt distribution is generated according to the radial dispersion distribution angle tilt vector; and characteristic clusters are generated according to the angular tilt distribution to determine the spatial distribution characteristics of the beam axis.

[0007] Furthermore, the process involves identifying optical axis offset patterns and abnormal offset points based on the spatial distribution characteristics of the beam axis, calibrating the real-time adjustment angle and displacement of the lens optical axis according to the identified offset patterns, and generating a lens calibration instruction set based on the adjustment angle and displacement, including: By identifying distribution clusters based on the spatial distribution characteristics of the beam axis, matching mode templates, and generating isolated nodes of abnormal offset points; by filtering noise thresholds based on the isolated nodes, generating classification labels for offset modes; by adjusting angle increments based on the classification labels, fusing dynamic weights, and generating a calibration angle set; by quantizing displacement vectors based on the calibration angle set, generating a calibration displacement set; and by generating an instruction sequence based on the calibration displacement set and mode parameters, outputting a lens calibration instruction set.

[0008] Furthermore, if the adjustment angle in the lens calibration instruction set exceeds a preset angle threshold, the lens optical axis position is dynamically adjusted according to the optical axis offset amplitude and direction to compensate for the beam axis offset in real time, thereby obtaining the calibrated optical axis parameters, including: The adjustment angle value is obtained from the lens calibration instruction set, and the threshold state is determined. The offset processing is activated according to the threshold state to generate an activated offset set. The amplitude spectrum is evaluated according to the activated offset set to generate a direction vector. The compensation chain is fused according to the direction vector to generate a dynamic compensation body. The position nodes are adjusted according to the dynamic compensation body to generate a compensated optical axis position. The stability index is fused according to the compensated optical axis position to generate the calibrated optical axis parameters.

[0009] Furthermore, the step of analyzing the propagation path of light in the lens through the calibrated optical axis parameters, performing ray tracing simulation on the propagation path to generate a light distribution dataset, and determining the color temperature distribution uniformity based on the luminous spectrum characteristics of the LED chip includes: The axial parameter set is analyzed from the calibrated optical axis parameters to construct the light propagation chain and generate the propagation path; the simulation field is simulated according to the propagation path to generate the simulation path; a distribution lattice is generated according to the simulation path, spectral lines are mapped, and the light distribution dataset is generated; the emission spectrum is fused according to the light distribution dataset to evaluate the color temperature distribution and generate the color temperature distribution uniformity.

[0010] Furthermore, after generating the light distribution dataset, the process includes: The emission spectrum is fused based on the light distribution dataset to generate a deviation range; the color temperature distribution is evaluated based on the deviation range to generate a uniformity index; a uniformity threshold is determined based on the uniformity index to generate color temperature distribution uniformity; a deviation description is determined based on the color temperature distribution uniformity, and the deviation value of the color temperature distribution uniformity is output.

[0011] Furthermore, the spectral consistency is determined by establishing a stable beam center axis distribution based on the calibrated optical axis parameters and color temperature distribution uniformity, and a comprehensive performance evaluation dataset is obtained, including: The axial index group is integrated from the calibrated optical axis parameters, and the uniform spectrum of the color temperature distribution uniformity is fused to generate a central axis distribution; a spectral consistency chain is determined based on the central axis distribution to generate spectral consistency; the stable interval is evaluated based on the spectral consistency, and the power spectral domain is fused to generate comprehensive consistency; an evaluation matrix is ​​generated based on the comprehensive consistency, and the performance evaluation dataset is output.

[0012] A real-time status analysis system for production equipment based on edge computing, the system comprising: The first generation module is used to obtain the position information of the LED chip mounting process. The XY coordinate offset and rotation angle offset of the chip relative to the substrate are obtained in real time through the machine vision system, and the mounting position deviation distribution dataset is generated. The conversion module is used to extract the offset angle and displacement vector of the LED chip from the mounting position deviation distribution dataset, and to convert the offset trajectory of the beam center axis from the chip offset angle and displacement vector according to the chip packaging structure and light source geometry. The first determining module is used to analyze the dynamic change trend of the beam center axis based on the offset trajectory of the beam center axis, and determine the spatial distribution characteristics of the beam axis from the dynamic change trend of the beam center axis in combination with the SIC method of LED array arrangement. The second generation module is used to identify optical axis offset patterns and abnormal offset points through the spatial distribution characteristics of the beam axis, calibrate the real-time adjustment angle and displacement of the lens optical axis according to the identified offset patterns, and generate a lens calibration instruction set based on the adjustment angle and displacement. The calibration module is used to dynamically adjust the position of the lens optical axis according to the optical axis offset amplitude and direction if the adjustment angle in the lens calibration instruction set exceeds the preset angle threshold, to compensate for the beam axis offset in real time, and obtain the calibrated optical axis parameters. The judgment module is used to analyze the propagation path of light in the lens through the calibrated optical axis parameters, perform ray tracing simulation processing on the propagation path, generate a light distribution dataset, and judge the uniformity of color temperature distribution based on the emission spectrum characteristics of the LED chip. The second determining module is used to determine the position of the target lens based on the uniformity of color temperature distribution and by adjusting the lens position attribute. The evaluation module is used to determine the stable beam center axis distribution and spectral consistency based on the calibrated optical axis parameters and color temperature distribution uniformity, and to obtain a performance evaluation dataset through comprehensive evaluation.

[0013] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a real-time analysis method for the status of production equipment based on edge computing. Addressing the issues of beam center axis offset and uneven color temperature distribution caused by chip mounting deviations in LED array packaging, the method generates a dataset of mounting position deviation distribution by real-time monitoring of the XY coordinate offset and rotation angle offset of the chip relative to the substrate, and then calculates the offset trajectory of the beam center axis. By analyzing the dynamic change trend and spatial distribution characteristics of the beam axis, this invention identifies optical axis offset patterns and anomalies, generates a lens calibration instruction set, and dynamically adjusts the lens optical axis position to compensate for the offset. Combined with ray tracing simulation and color temperature distribution uniformity analysis, it optimizes spectral consistency. By integrating machine vision, ray tracing, and dynamic calibration technologies, this invention achieves precise beam axis calibration and improved color temperature uniformity, significantly improving the optical performance and packaging quality of LED arrays, and providing an efficient solution for high-precision LED packaging. Attached Figure Description

[0014] Figure 1This is a flowchart of a real-time analysis method for the status of production equipment based on edge computing according to the present invention.

[0015] Figure 2 This is a schematic diagram of the specific process of step S101 in an embodiment of the present invention.

[0016] Figure 3 This is a schematic diagram of the specific process of step S102 in an embodiment of the present invention.

[0017] Figure 4 This is a schematic diagram of the specific process of step S103 in an embodiment of the present invention.

[0018] Figure 5 The diagram illustrates a block diagram of a real-time status analysis system for production equipment based on edge computing, as an exemplary embodiment. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] See Figure 1 This embodiment of a real-time analysis method for the status of production equipment based on edge computing may specifically include: S101. Obtain the position information of the LED chip mounting process, and use the machine vision system to monitor in real time the XY coordinate offset and rotation angle offset of the chip relative to the substrate during the mounting process, and generate a mounting position deviation distribution dataset.

[0021] In one implementation, such as Figure 2 As shown, the above steps include: S1011. The machine vision system acquires images of the chip position to generate raw pixel data; an edge detection algorithm is used to determine the chip edge contour based on the raw pixel data to obtain the center coordinate value of the chip edge contour. S1012. Based on the comparison between the center coordinate value and the substrate reference point, calculate the torsional difference of the rotation angle and the vector component of the coordinate offset; generate classification labels based on the torsional difference and the vector component. S1013. Based on the historical sequence of angle deviations integrated by the classification labels, a time axis correlation matrix is ​​constructed; clustering is performed based on the time axis correlation matrix to generate statistical features of cluster groups. S1014. Based on the statistical features, fuse the dynamic path of the mounting process to generate a multidimensional deviation mapping and output the mounting position deviation distribution dataset. In another implementation, a machine vision system acquires images of the chip's position for monitoring. The acquired images provide the raw pixel data needed to obtain the coordinate offset. An edge detection algorithm is then used to determine the chip's edge contour based on this raw pixel data, yielding the center coordinates of the chip's edge contour. The edge detection algorithm involves calculating and identifying boundaries using pixel gradients. The raw pixel data is input, and the chip's edge contour is output. The torsional difference of the rotation angle is calculated by comparing the center coordinates with a substrate reference point. The quantized vector component of the offset is extracted from this torsional difference. It is then determined whether the vector component exceeds a preset threshold. If it does, an anomaly is marked, and a classification label for the anomaly is obtained. The historical sequence of the angle deviation records is integrated based on these classification labels. A time-axis correlation matrix is ​​constructed from this historical sequence. Clustering is performed using this time-axis correlation matrix to determine the cluster groups constructed from the distributed data, yielding the statistical characteristics of the cluster groups. The time-axis correlation matrix is ​​a two-dimensional array formed by arranging historical sequences in chronological order. The historical sequence is input, and the cluster groups are output. Based on the statistical features, the dynamic path tracked in the mounting process is fused, a multidimensional deviation mapping is generated from the dynamic path, and the structure generated by the deviation dataset is output for the multidimensional deviation mapping to obtain the mounting position deviation distribution dataset of the structure.

[0022] Specifically, in one implementation, image acquisition is performed on the chip position monitoring using a machine vision system.

[0023] Specifically, the machine vision system includes a camera and an image processor. It acquires chip images during the mounting process and obtains the raw pixel data required for coordinate offset from the images. For the raw pixel data, an edge detection algorithm is used to determine the chip's edge contour. This algorithm identifies boundaries by calculating pixel gradients; for example, gradient calculation involves differentiating the grayscale differences between adjacent pixels. After inputting the raw pixel data, it outputs the center coordinates of the chip's edge contour. This process ensures real-time capture of the chip's XY coordinate offset relative to the substrate. Further, based on a comparison of the center coordinates with a substrate reference point, a torsional difference is calculated for rotation angle detection. The torsional difference refers to the angular difference between the center coordinates and the reference point, obtained through vector operations. The offset quantization vector components are extracted from the torsional difference, including X-axis and Y-axis components. It is determined whether the vector components exceed a preset threshold; if so, an anomaly is marked, resulting in an anomaly classification label. This label is used to distinguish between normal offsets and abnormal deviations, applied in automotive lighting LED mounting scenarios to prevent beam deviation.

[0024] Preferably, a time-axis correlation matrix is ​​constructed from the historical sequences of the integrated angle deviation records for the classification labels. The time-axis correlation matrix is ​​a two-dimensional array formed by arranging the historical sequences in chronological order; for example, rows represent time points, and columns represent deviation parameters. The construction process involves sequence sorting and matrix filling. After inputting the historical sequences, the deviation correlation is represented by the matrix. Clustering is then performed using the time-axis correlation matrix. Clustering refers to using the K-means algorithm to group similar deviations. The K-means algorithm achieves grouping by iteratively calculating cluster centers, determining the cluster groups constructed from the distributed data, and obtaining the statistical characteristics of the cluster groups, such as mean and variance.

[0025] For example, a multidimensional deviation map is generated from the dynamic path tracked during the chip mounting process by fusing statistical features. The dynamic path refers to a continuous record of the chip mounting trajectory. The fusion process overlays statistical features onto the path data, for example, through weighted averaging, to generate a multidimensional deviation map including XY offsets and rotations. The final structure generated from the deviation dataset output by the multidimensional deviation map yields the final structure's mounting position deviation distribution dataset. This dataset is used in commercial lighting fixture production for subsequent optical axis calibration to ensure spectral consistency.

[0026] In one possible implementation, the above process can be applied to high-end LED array mounting. A machine vision system acquires multi-chip images, an edge detection algorithm processes batch data, the array arrangement is considered when calculating the torsion difference, clustering processing integrates the sequence to identify patterns, and a deviation map is generated to support dynamic compensation, thereby achieving beam center axis stabilization. Further...

[0027] It should be noted that the construction of the time axis correlation matrix can be extended to historical sequences that include rotation angles, and the clustering process adapts to different offset magnitudes by adjusting the number of clusters, ensuring that the statistical features reflect the true distribution of the deviation.

[0028] Specifically, when fusing statistical features of cluster groups with dynamic paths, the multidimensional deviation mapping can be represented as a three-dimensional coordinate system. When outputting the final structure, data compression is used to form a dataset of mounting position deviation distribution, which is used for optical lens configuration optimization.

[0029] S102. Extract the offset angle and displacement vector of the LED chip from the mounting position deviation distribution dataset, and calculate the offset trajectory of the beam center axis from the chip offset angle and displacement vector according to the chip packaging structure and light source geometry.

[0030] In one implementation, such as Figure 3 As shown, the above steps include: S1021. Extract the initial angle value of the offset angle and the displacement component of the displacement vector from the mounting position deviation distribution dataset, construct vector angle pairs, and generate a composite deviation set. S1022. Based on the chip shell parameters of the chip packaging structure analyzed by the composite deviation set, the curvature radius data of the geometric characteristics of the light source are fused to generate a structural fusion body; S1023. By mapping the encapsulated geometry through the structural fusion body, a preliminary offset path for the beam axis is determined; the path nodes are adjusted according to the preliminary offset path to generate a corrected offset path. S1024. Based on the modified offset path, process the conversion factor to generate an axial sequence; perform trajectory smoothing based on the axial sequence to generate a continuous trajectory line; determine the offset trajectory of the beam center axis based on the continuous trajectory line and the deviation data filtering threshold. In another implementation, initial angle values ​​are extracted from the mounting position deviation distribution dataset. Displacement components quantized from the displacement vectors are integrated with these initial angle values, and vector-angle pairs are constructed using these displacement components to obtain a composite deviation set for the vector-angle pairs. The chip casing parameters derived from the package structure are analyzed based on the composite deviation set. Curvature radius data from the geometric characteristic fusion is obtained from the chip casing parameters. The correlation weights associated with the angle vectors are fused with the curvature radius data to obtain a structural fusion body with the correlation weights. The structural fusion body is used to map the package geometry mapped by the structural characteristics. An initial offset path for the beam axis offset is determined from the package geometry. If the initial offset path exceeds a preset threshold, the path nodes are adjusted to obtain a corrected offset path for the path nodes. The trajectory conversion factor is processed for the corrected offset path. An axial sequence generated from the axial trajectory is obtained from the conversion factor. The axial sequence is used for trajectory smoothing to obtain a smoothed continuous trajectory line. Trajectory smoothing refers to the process of achieving path continuity by averaging adjacent sequence points. Based on the filtering threshold of the deviation data fused with the continuous trajectory line, the offset trajectory of the beam center axis is determined from the filtering threshold, and the trajectory distribution of the offset trajectory is obtained.

[0031] Specifically, in one implementation, the initial angle value of the offset angle is extracted from the mounting position deviation distribution dataset.

[0032] Specifically, the initial angle value is obtained through dataset filtering. A displacement component, representing the quantized components in the XY directions, is integrated with this initial angle value. Vector-angle pairs are constructed using these components; each pair is a set of paired angles and displacements, resulting in a composite deviation set. This set is used to quantify minute offsets during LED chip mounting. Further, the chip housing parameters, including thickness and shape data, are analyzed based on the composite deviation set. Curvature radius data, reflecting the light source's bending, is obtained from these parameters through geometric fusion. Angle vector correlation weights are then fused with the curvature radius data; these weights are numerical values ​​weighted by the correlation between angles and vectors, resulting in a fused structure of correlation weights. This fused structure supports preliminary beam path modeling.

[0033] Preferably, the encapsulation geometry is mapped by mapping the structural characteristics of the structural fusion body. The encapsulation geometry is a representation of the fusion body projected onto a two-dimensional graphic. An initial offset path for the beam axis deviation is determined from the encapsulation geometry. This initial offset path is based on the trajectory of the lines connecting the nodes in the graphic. If the initial offset path exceeds a preset threshold, the path nodes are adjusted. This adjustment involves shifting the node positions to conform to the threshold, resulting in a corrected offset path for the path nodes. This path achieves deviation compensation in automotive headlight LED configurations.

[0034] For example, a conversion factor is used for trajectory conversion processing to correct offset paths. The conversion factor is a scaling factor that converts the path into a trajectory. An axial sequence for generating the axial trajectory is obtained from the conversion factor. This axial sequence is an ordered set of points along the central axis. Trajectory smoothing is performed using this axial sequence. Trajectory smoothing is the process of achieving path continuity by averaging adjacent sequence points. For example, averaging involves taking the median of adjacent points to reduce noise, resulting in a smooth, continuous trajectory line. In commercial lighting fixture production, this line ensures uniform spectral distribution.

[0035] In one possible implementation, the above process can be applied to high-end LED array packaging. When extracting initial angle values ​​from the dataset, the offset of multiple chips in the array is considered. A composite deviation set is constructed to fuse batch data. Chip shell parameters are analyzed and extended to a multi-layer structure. When obtaining curvature radius data, array geometry is integrated. Associative weights are fused to support overall beam modeling. The package geometry is mapped to process complex layers. An initial offset path is determined and nodes are adjusted to accommodate array deviations. Conversion factors are processed to obtain the axial sequence. The smoothed trajectory line output is continuously distributed, achieving optical axis stabilization. Further...

[0036] It should be noted that the trajectory smoothing process can be extended to axial sequences that include displacement components. A weighted average is used when averaging adjacent sequence points to highlight key offsets and ensure that continuous trajectory lines reflect the true beam dynamics.

[0037] Specifically, a filtering threshold is used to filter deviation data based on continuous trajectory lines. This threshold serves as a boundary value for filtering deviations. The offset trajectory of the beam center axis is determined from this threshold, and the resulting trajectory distribution is obtained after threshold filtering. This distribution provides a precise description of the beam center axis offset in lighting fixture assembly, supporting lens calibration.

[0038] In one embodiment, when fusing the screening threshold, a multi-level threshold can be introduced, with threshold levels set for different offset magnitudes, and the offset trajectory can be finely determined from the multi-level thresholds, with the trajectory distribution covering radial and axial offset details.

[0039] It is understandable that the extraction of the deviation dataset and the formation of the trajectory distribution form a logical coherence. In the LED light source mounting scenario, these steps are used to convert the chip offset to the beam trajectory and obtain the offset trajectory for optical matching.

[0040] S103. Analyze the dynamic change trend of the beam center axis based on the offset trajectory of the beam center axis, and determine the spatial distribution characteristics of the beam axis by combining the SIC arrangement of the LED array with the dynamic change trend of the beam center axis.

[0041] In one implementation, such as Figure 4 As shown, the above steps include: S1031. Extract a trend sequence from the offset trajectory of the beam center axis and construct a trend variation map; fuse the LED array coordinates according to the trend variation map to generate a layout grid; S1032. Generate a fusion trend body by integrating the correlation chain of the offset trend of the grid arrangement; quantify the axial interval based on the fusion trend body to determine the radial dispersion. S1033. Generate an angular tilt distribution based on the radial dispersion distribution angle tilt vector; generate a characteristic cluster based on the angular tilt distribution, and determine the spatial distribution characteristics of the beam axis; In another implementation, a trend sequence of dynamic change trends is extracted from the offset trajectory of the beam center axis. A set of variation points is analyzed based on the trend sequence to determine the trajectory changes. A trend variation map is constructed using these variation points to obtain the dynamic change trend of the trend variation map. The trend variation map is a graphical representation formed by connecting point sets. The LED array coordinates of the fused array arrangement are fused according to the dynamic change trend. The arrangement grid integrated in the SIC method is obtained from the LED array coordinates. The correlation chains associated with the offset trend are integrated into the arrangement grid to obtain the fusion trend body of the correlation chains. SIC integration refers to the process of integrating coordinates according to the silicon-based integrated circuit layout. The axial range is quantified into an axial interval using the fusion trend body. The dispersion ring domain for radial dispersion evaluation is determined from the axial interval. If the dispersion ring domain exceeds a preset threshold, the ring domain boundary is adjusted to obtain the radial dispersion degree of the ring domain boundary. The tilt vector of the angular tilt distribution of the radial dispersion degree distribution is used to obtain the axial projection of the beam axis characteristics from the tilt vector. Vector clustering is performed using the axial projection to obtain the angular tilt distribution of the vector cluster. Vector clustering refers to the process of grouping similar vectors, which is achieved by calculating vector distances. Based on the angular tilt distribution, characteristic clusters are generated to determine the spatial distribution characteristics of the beam axis from these characteristic clusters, thus obtaining a description set of the spatial distribution characteristics.

[0042] Specifically, in one implementation, a trend sequence of dynamic trends is extracted from the offset trajectory of the beam's central axis.

[0043] Specifically, the trend sequence is obtained through trajectory point sampling. The set of variation points, which represents the collection of abrupt changes in the sequence, is analyzed based on the trajectory changes of the trend sequence. A trend variation map is constructed using this set of variation points; the trend variation map is a graphical representation formed by connecting these point sets, yielding the dynamic change trend of the trend variation map. This trend is used to capture beam drift patterns in LED light source mounting. Further, the LED array coordinates are fused based on the dynamic change trend. The LED array coordinates reflect the spatial position of the chips. From the LED array coordinates, a SiC-integrated layout grid is obtained. SiC integration refers to the process of integrating coordinates according to the layout of silicon-based integrated circuits. For example, the layout involves grid division to match the silicon substrate. The layout grid is then used to integrate the offset trend-related association chains. These association chains are sequences linked by trends and coordinates, resulting in a fused trend body of the association chains. This body supports dynamic response analysis in automotive lighting LED arrays.

[0044] Preferably, the axial range is quantified by fusing trend volume. The axial range is the offset limit along the central axis. From the axial range, a dispersion ring domain for radial dispersion assessment is determined. The dispersion ring domain refers to a radially expanding annular area. If the dispersion ring domain exceeds a preset threshold, the ring domain boundary is adjusted. The ring domain boundary adjustment involves shrinking or expanding the ring domain to meet the threshold, resulting in the radial dispersion of the ring domain boundary. This dispersion quantifies the degree of light scattering in commercial lighting scenarios.

[0045] For example, consider the tilt vector of the radial dispersion distribution angle tilt distribution. The tilt vector is a vector representation that maps dispersion to angle. The axial projection of the beam axis characteristics is obtained from the tilt vector. The axial projection is derived by projecting the vector along the axis. Vector clustering is performed using the axial projection. Vector clustering refers to the process of grouping similar vectors, achieved by calculating vector distances, such as Euclidean distance, to identify clusters, resulting in the angular tilt distribution of the vector clusters. In high-end lighting fixture manufacturing, this distribution describes the beam tilt variation.

[0046] In one possible implementation, the above process can be applied to multi-LED array arrangement, processing batch trajectories when extracting trend sequences, constructing trend variation maps to fuse array data, obtaining arrangement grids extended to three-dimensional silicon-based layouts, integrating correlation chains to support overall trends, quantifying axial intervals to consider inter-array interference, determining dispersed ring domains and adjusting boundaries to accommodate deviations, distributing tilt vectors to obtain axial projections, performing vector clustering to output tilt distributions, generating characteristic clusters to determine spatial characteristics, and achieving beam distribution optimization. Further...

[0047] It should be noted that the vector clustering process can be extended to axial projection that includes radial dispersion. When calculating vector distance, a standardized distance is used to balance different dimensions and ensure that the angular tilt distribution accurately reflects the spatial tilt.

[0048] Specifically, a characteristic cluster is generated based on the angular tilt distribution to produce distribution characteristics. A characteristic cluster is a set of grouped distributions. From the characteristic cluster, the spatial distribution characteristics of the beam axis are determined. These spatial distribution characteristics include axial offset range, radial dispersion, and angular tilt distribution, resulting in a descriptive set of spatial distribution characteristics. This set provides distribution details in lighting fixture assembly, supporting lens adjustment.

[0049] In one embodiment, a multi-cluster algorithm can be introduced when generating characteristic clusters, setting cluster parameters for different tilt amplitudes, and finely determining spatial distribution characteristics from multiple clusters, with the description set covering multi-dimensional offset indices.

[0050] S104. Identify optical axis offset patterns and abnormal offset points through the spatial distribution characteristics of the beam axis, calibrate the real-time adjustment angle and displacement of the lens optical axis according to the identified offset patterns, and generate a lens calibration instruction set based on the adjustment angle and displacement.

[0051] In one embodiment, the above steps include: S1041. Identify distribution clusters based on the spatial distribution characteristics of the beam axis, match pattern templates, and generate isolated nodes of abnormal offset points. S1042. Generate a classification label for the offset pattern based on the noise filtering threshold of the isolated node; adjust the angle increment based on the classification label, fuse dynamic weights, and generate a calibration angle set. S1043. Quantize the displacement vector according to the calibration angle set, and generate a calibration displacement set; generate an instruction sequence according to the calibration displacement set and mode parameters, and output the lens calibration instruction set. In another implementation, the spatial distribution clusters identified by the spatial distribution characteristics of the beam axis are identified. A pattern template for offset pattern identification is matched against these clusters. Isolated nodes for anomaly point localization are obtained from the pattern template, resulting in anomaly offset points for these isolated nodes. The pattern template refers to a set of preset offset patterns, and the matching process is achieved by comparing the similarity between the clusters and the template. A noise threshold for filtering the anomaly offsets is filtered based on these anomaly offset points. A classification label for the optical axis pattern is determined from the noise threshold. If the classification label exceeds a preset threshold, the label group is reclassified to obtain the offset pattern of the label group. Reclassifying the label group refers to the process of regrouping labels to correct deviations. The angle increment of the calibration angle adjustment is adjusted for the offset pattern. Dynamic weights from the real-time adjustment are fused into the angle increment, and the real-time adjustment angle of the lens optical axis is calibrated using these dynamic weights to obtain a calibration angle set for the real-time adjustment angle. The displacement vector calibrated based on the calibration angle set is quantized. A matching offset for the distribution characteristics is obtained from the displacement vector, and the displacement of the lens optical axis is determined from the matching offset to obtain a calibration displacement set for the displacement. The instruction sequence generated by the instruction set is generated by the calibration displacement set, and the mode parameters of the optical axis mode classification are fused from the instruction sequence to output the lens calibration instruction set.

[0052] Specifically, in one embodiment, spatial distribution clusters are identified by the spatial distribution characteristics of the beam axis.

[0053] Specifically, the distribution clusters are derived through feature clustering. A pattern template for offset pattern identification is then used to match the distribution clusters. The pattern template refers to a set of preset offset patterns. The matching process is achieved by comparing the similarity between the cluster and the template. For example, similarity comparison involves calculating the sum of the distances between cluster points and template points. Isolated nodes for locating abnormal points are obtained from the pattern template. Isolated nodes are misaligned points in the matching process, resulting in abnormal offset points. These points are used to detect abnormal beam drift in LED chip mounting. Further, a noise threshold is used to filter abnormal offsets based on these abnormal offset points. The noise threshold is the boundary for excluding minor deviations. Classification labels for optical axis patterns are determined from the noise threshold. These labels are identifiers for classifying offset points. If a classification label exceeds a preset threshold, the label group is reclassified. Reclassifying the label group refers to the process of regrouping labels to correct deviations, resulting in the offset pattern of the label group. This pattern is used in automotive lighting lens calibration to classify optical axis deviation types.

[0054] Preferably, the angle increment for adjusting the calibration angle is adjusted according to the offset mode. The angle increment is the adjustment amount calculated based on the mode. The dynamic weight of real-time adjustment is fused from the angle increment. Dynamic weight fusion refers to the process of weighting the angle increment to reflect real-time changes. For example, weighting involves multiplying by a weight coefficient to balance the influence of different modes. The real-time adjustment angle of the lens optical axis is calibrated using dynamic weight fusion to obtain the calibration angle set for real-time adjustment.

[0055] For example, a displacement vector is calibrated based on the calibrated angle set to quantize the displacement. The displacement vector is a representation of the angle set converted into a spatial displacement. A matching offset for matching the distribution characteristics is obtained from the displacement vector. The matching offset is derived by aligning the vector with the distribution characteristics. The displacement of the lens optical axis is determined from the matching offset. The displacement is the calibrated displacement distance, resulting in a calibrated displacement set. In high-end lighting assembly scenarios, this set quantizes the lens displacement to compensate for chip misalignment.

[0056] In one possible implementation, the above process can be applied to LED array lens configuration, handling multi-axis characteristics when identifying distributed clusters, extending the matching pattern template to multi-dimensional patterns, acquiring isolated nodes to support batch anomaly detection, filtering noise thresholds to consider array interference, determining classification labels and reclassifying to adapt to complex patterns, adjusting angle increments and fusing dynamic weights to calibrate real-time angles, quantizing displacement vectors to obtain matching offsets and determine displacement amounts, generating instruction sequences, fusing mode parameters, and outputting an instruction set to achieve universality in lens calibration. Further...

[0057] It should be noted that the dynamic weight fusion process can be extended to include weighting of offset modes. When multiplying by the weight coefficient, a proportional allocation is used to highlight the dominant deviation and ensure that the calibration angle set accurately reflects the real-time requirements.

[0058] Specifically, a sequence of instructions is generated by calibrating the displacement set to produce an instruction set. The instruction sequence is a chain of commands that encodes the displacement set. The mode parameters for optical axis mode classification are fused from the instruction sequence. The mode parameters are quantized values ​​of the classification labels. A lens calibration instruction set is output based on the mode parameters. The instruction set is a set of commands to adjust the lens, resulting in the instruction set of the lens calibration instruction set.

[0059] In one embodiment, a parameter hierarchy can be introduced when generating the instruction sequence, and a fusion level can be set for different modes. From the hierarchy, a lens calibration instruction set is finely output, which covers angle and displacement details.

[0060] S105. If the adjustment angle in the lens calibration instruction set exceeds the preset angle threshold, the lens optical axis position is dynamically adjusted according to the optical axis offset amplitude and direction to compensate for the beam axis offset in real time and obtain the calibrated optical axis parameters.

[0061] In one embodiment, the above steps include: S1051. Obtain the adjustment angle value from the lens calibration instruction set and determine the threshold state; activate the offset processing according to the threshold state and generate the activated offset set. S1052. Evaluate the amplitude spectrum based on the activated offset set and generate a direction vector; fuse the compensation chain based on the direction vector and generate a dynamic compensation body; S1053. Adjust the position nodes according to the dynamic compensator to generate the compensated optical axis position; generate the calibrated optical axis parameters according to the fusion stability index of the compensated optical axis position. In another implementation, an adjustment angle value is obtained from a lens calibration instruction set. A threshold state of the instruction set threshold is determined based on the adjustment angle value. If the threshold state exceeds a preset threshold, offset processing is activated, resulting in an activated offset set. The amplitude spectrum of the offset amplitude assessment is evaluated based on the activated offset set. A direction vector for dynamic direction adjustment is determined from the amplitude spectrum. This direction vector is then used to fuse a compensation chain for real-time offset compensation, resulting in a dynamic compensation body for the compensation chain. The amplitude spectrum refers to the evaluation represented by the offset set spectrum, and compensation chain fusion refers to the process of linking vectors to form continuous compensation. The position node of the optical axis position compensation is adjusted through the dynamic compensation body. An angle correction obtained from the calibration angle is obtained from the position node. The beam axis offset is compensated based on the angle correction, resulting in a compensated optical axis position for the beam axis offset. The displacement interval determined by the compensated optical axis position is determined. A stability index for axial stability assessment is fused from the displacement interval. The stability index is used to quantify the axial stability, resulting in a stability value for the axial stability. Stability index fusion refers to the process of merging interval data to calculate stability. The parameter set output by the stability value is used to integrate the calibration angle and compensation displacement obtained by the calibration angle from the parameter set, and the integrated parameter set generates the calibrated optical axis parameters.

[0062] Specifically, in one implementation, the adjustment angle value for angle threshold determination is obtained from the lens calibration instruction set.

[0063] Specifically, the adjustment angle value is obtained through instruction set parsing. The threshold state of the instruction set threshold is then determined based on the adjustment angle value. This threshold state is a comparison between the angle and a preset threshold. If the threshold state exceeds the preset threshold, offset processing is activated. Offset processing involves initiating a compensation mechanism, resulting in an activated offset set. This set is used to trigger dynamic calibration in LED light source lens configurations. Further, the amplitude spectrum of the offset amplitude is evaluated based on the activated offset set. The amplitude spectrum refers to the evaluation of the offset set's spectral representation. For example, the spectrum representation is obtained by transforming the offset data into an amplitude distribution. The direction vector for dynamic adjustment is determined from the amplitude spectrum. The direction vector is the quantization of the dominant direction in the spectrum. A compensation chain for real-time offset compensation is used by fusing the direction vectors. Compensation chain fusion refers to the process of linking vectors to form continuous compensation. For example, linking is achieved by concatenating vector endpoints to create a continuous path, resulting in a dynamic compensation body. This body processes the optical axis offset amplitude in lighting production.

[0064] Preferably, the position node for optical axis position compensation is adjusted by a dynamic compensator. The position node is a key point in the compensator. Angle corrections, derived from calibration angles, are obtained from the position node. These angle corrections are calculated by rotating the node. To compensate for beam axis offset, the compensation involves shifting nodes to align with the axis, resulting in the compensated optical axis position for beam axis offset. This position ensures real-time lens position stability in the lighting scenario.

[0065] For example, the displacement range determined by the compensation displacement is determined based on the compensated optical axis position. The displacement range is the extended range of the optical axis position. A stability index for axial stability assessment is fused from the displacement range. Stability index fusion refers to the process of merging interval data to calculate stability, such as merging the index obtained by averaging interval values. The stability index is used to quantify axial stability, and axial stability is the numerical expression of the index, resulting in a stability value for axial stability. In lamp assembly, this value quantifies the beam axis stability.

[0066] In one possible implementation, the above process can be applied to a multi-lens LED system. When acquiring adjustment angle values, batch instructions are parsed; threshold states are determined to activate the offset set to support array processing; the amplitude spectrum is evaluated to determine the direction vector fusion compensation chain to adapt to complex offsets; position nodes are adjusted to obtain angle correction compensation axis offsets; displacement intervals are determined to fuse stability indices and quantify stability; and the output parameter set integrates the calibration angle and compensation displacement to generate optical axis parameters, thus achieving optical compensation. Further...

[0067] It should be noted that the compensation chain fusion process can be extended to links that include direction vectors. When connecting vector endpoints, sequential connections are used to maintain path continuity and ensure that the dynamic compensation body accurately captures the offset dynamics.

[0068] Specifically, the parameter set output by the stability value output parameter is a set of stability codes. The calibration angle and compensation displacement obtained by the calibration angle are integrated from the parameter set. The integration involves combining the angle and displacement into a unified parameter. The integrated and calibrated optical axis parameters are generated. The optical axis parameters include the calibration angle, compensation displacement and axial stability, thus obtaining the parameter set of optical axis parameters.

[0069] S106. Analyze the propagation path of light in the lens by calibrating the optical axis parameters, perform ray tracing simulation on the propagation path, generate a light distribution dataset, and determine the uniformity of color temperature distribution based on the luminous spectrum characteristics of the LED chip.

[0070] In one embodiment, the above steps include: S1061. Analyze the axial parameter set from the calibrated optical axis parameters, construct the light propagation chain, and generate the propagation path; simulate the simulation field based on the propagation path and generate the simulation path; S1062. Generate a distribution matrix based on the simulation path, map spectral lines, and generate the light distribution dataset; fuse the emission spectrum based on the light distribution dataset, evaluate the color temperature distribution, and generate color temperature distribution uniformity. In another implementation, after generating the light distribution dataset, the process includes: The emission spectrum is fused based on the light distribution dataset to generate a deviation range; the color temperature distribution is evaluated based on the deviation range to generate a uniformity index; a uniformity threshold is determined based on the uniformity index to generate color temperature distribution uniformity; a deviation description is determined based on the color temperature distribution uniformity, and the deviation value of the color temperature distribution uniformity is output.

[0071] In another implementation, the axial parameter set of the calibrated optical axis parameters is parsed, and a path node set for the propagation path is traced for the axial parameter set. A ray propagation chain is constructed using the path node set to obtain the propagation path of the ray propagation chain. A simulation field for ray simulation is generated based on the propagation path. An association vector associated with the parameter path is obtained from the simulation field. If the association vector exceeds a preset threshold, the vector node is optimized to obtain the simulation path of the vector node. An optimization branch of the simulation path optimization is processed through the simulation path, and a distribution matrix generated from the distribution dataset is generated from the optimization branch. The distribution matrix is ​​mapped to the spectral mapping of the dataset to obtain the ray distribution dataset of the spectral lines. An emission spectrum is obtained by fusing the spectral characteristics of the ray distribution dataset. A deviation range for the uniformity deviation assessment is determined from the emission spectrum. The color temperature distribution is evaluated using the deviation range to obtain a uniformity index for the color temperature distribution. A uniformity threshold for the color temperature uniformity assessment is determined based on the uniformity index. A deviation description for the uniformity deviation assessment is output from the uniformity threshold to obtain the color temperature distribution uniformity described by the deviation.

[0072] Specifically, in one implementation, the axial parameter set is analyzed from the calibrated optical axis parameters.

[0073] Specifically, the axial parameter set is derived through parameter decomposition. A path node set is then used to trace the propagation path of the axial parameter set. This path node set refers to the collection of light trajectory points within the parameter set. A light propagation chain is constructed using this path node set. A light propagation chain is a chain formed by sequentially connecting nodes; for example, connecting nodes sequentially to simulate light flow yields the propagation path of the light propagation chain. This path is used in LED lens configurations to analyze light propagation behavior. Further, a simulation field is created based on the propagation path to simulate the light propagation. The simulation field is a representation of the path in a virtual environment. Association vectors linking the parameter paths are obtained from the simulation field. These association vectors connect field points to parameters. If the association vector exceeds a preset threshold, the vector nodes are optimized. Optimizing vector nodes involves adjusting node positions to meet the threshold, for example, by adjusting by shifting nodes to reduce deviation, thus obtaining the simulation path of the vector nodes.

[0074] Preferably, the optimization branches of the simulation path optimization are processed through simulation path processing. The optimization branches are bifurcation variations of the path. A distribution matrix is ​​generated from the optimization branches, which is a matrix representation formed by branch sampling. The distribution matrix is ​​then mapped to the spectral lines of the spectral mapping dataset. Spectral line mapping involves projecting the matrix onto spectral coordinates to obtain the spectral line light distribution dataset. This dataset describes the spatial distribution of light in commercial lighting scenarios.

[0075] For example, an emission spectrum is fused based on spectral characteristics from a light distribution dataset. The emission spectrum is a graphical representation of the LED chip spectrum. From the emission spectrum, a deviation range for uniformity deviation assessment is determined. This deviation range is obtained by comparing the spectrum with the dataset. The color temperature distribution is assessed using this deviation range assessment. Deviation range assessment refers to the process of calculating the variance within the range to quantify the distribution. For example, variance calculation involves calculating the sum of squares of the average deviations at points within the range to obtain a uniformity index for the color temperature distribution.

[0076] In one possible implementation, the above process can be applied to a multi-LED light source lens system. When resolving the axial parameter set, batch parameters are processed; a path node set is traced to construct a propagation chain to support array simulation; the simulated field is used to obtain associated vectors and optimize nodes to adapt to complex paths; the optimized branches are processed to generate a distributed dot matrix mapping spectral output dataset; the emission spectrum is fused to determine the deviation interval and evaluate the uniformity index; a uniformity threshold is determined to output a deviation description, achieving universality for color temperature uniformity judgment. Further...

[0077] It should be noted that the process of deviation interval assessment can be extended to include variance calculation of spectral lines. When calculating the sum of squares, weighted variance is used to emphasize key distributions and ensure that the uniformity index accurately quantifies color temperature variation.

[0078] Specifically, a uniformity threshold is used to judge the uniformity of color temperature based on the uniformity index. The uniformity threshold is the comparison boundary between the index and a preset value. A deviation description for uniformity deviation assessment is output from the uniformity threshold. The deviation description is a uniformity description filtered by the threshold, thus obtaining the uniformity of color temperature distribution described by the deviation.

[0079] S107. Determine the target lens position based on the uniformity of color temperature distribution by adjusting the lens position attribute.

[0080] In one possible implementation, step S107 includes the following sub-steps: S1071. Calculate the color temperature distribution uniformity deviation value based on the color temperature distribution uniformity. Based on the magnitude and distribution of the color temperature distribution uniformity deviation value, obtain the current focal length parameter and spatial coordinate information of the lens. Use interpolation to calculate the predicted value of the light intensity distribution corresponding to different lens positions.

[0081] S1072. By comparing the predicted light intensity distribution with the target light intensity, the optimal lens position parameters are determined using the gradient descent method. If the adjustment range of the lens position parameters exceeds the preset adjustment range, alternative lens position combinations are obtained for optimization.

[0082] S1073. Drive the stepper motor to adjust the spatial coordinates of the lens according to the optimal lens position parameters, obtain the adjusted color temperature distribution data to verify the color reproduction, judge the effectiveness of the lens position adjustment by the color reproduction value, and obtain the target lens position that meets the uniformity requirements.

[0083] Specifically, the current optical state information is obtained by monitoring the lens's focal length parameters and three-dimensional spatial coordinates in real time.

[0084] Specifically, when the color temperature distribution uniformity deviation is 15% and is mainly distributed on the left side of the illuminated area, the regulator records the lens's current focal length as 50mm, X-axis coordinate as 12.5mm, and Y-axis coordinate as 8.3mm. This precise positioning provides the basic data support for subsequent optical adjustments. Interpolation plays a crucial role in predicting illumination intensity, using known discrete lens positions to extrapolate the illumination distribution characteristics within a continuous range of positions.

[0085] In one possible implementation, when the lens moves from 10mm to 15mm along the X-axis, interpolation calculations can predict that the illumination intensity at position 12.5mm will reach 2800 lux, and at position 13.8mm, it will be 3200 lux. This predictive capability makes the adjustment process more accurate and efficient, avoiding the time wasted by blind adjustments. The gradient descent method finds the optimal lens position parameters step by step by calculating the gradient between the predicted illumination intensity distribution and the target illumination intensity.

[0086] For example, when the target illumination intensity is set to 3000 lux, the algorithm calculates the intensity difference in each direction. If the gradient in the positive X-axis direction is -200 and the gradient in the negative Y-axis direction is 150, the algorithm will guide the lens to move simultaneously in both the positive X-axis and negative Y-axis directions. This optimization method can quickly converge to the optimal position, significantly improving adjustment efficiency.

[0087] It should be noted that when the adjustment range of the lens position parameters exceeds the preset adjustment range, the alternative lens position combination mechanism will start to function.

[0088] Specifically, if the calculated optimal position requires an 8mm lens movement, but the preset adjustment range only allows a 5mm movement, then alternative position combinations will be activated. Multiple smaller adjustment steps will be used to gradually approach the optimal position, ensuring adjustment accuracy while avoiding excessive stress on the mechanical structure. The stepper motor, as a precision actuator, adjusts the spatial coordinates based on the optimal lens position parameters.

[0089] In one embodiment, when the lens needs to be adjusted from its current position to a target position of 14.2mm on the X-axis and 6.8mm on the Y-axis, the stepper motor moves incrementally with an accuracy of 0.01mm, pausing for 0.1 seconds after each movement to ensure mechanical stability. Color reproduction verification, as a key evaluation indicator of the adjustment effect, determines the effectiveness of the lens position adjustment by measuring the adjusted color temperature distribution.

[0090] For example, when the color reproduction value increases from 82% before adjustment to 94% after adjustment, it indicates that the lens position adjustment has achieved a significant effect.

[0091] S108. Determine the stable beam center axis distribution and spectral consistency based on the calibrated optical axis parameters and color temperature distribution uniformity, and obtain the performance evaluation dataset through comprehensive evaluation.

[0092] In one embodiment, the above steps include: S1081. Integrate the axial index group from the calibrated optical axis parameters, fuse the uniform spectrum of the color temperature distribution uniformity, and generate the central axis distribution. S1082. Determine the spectral consistency chain based on the central axis distribution to generate spectral consistency; evaluate the stability interval based on the spectral consistency, fuse the power spectral domain, and generate comprehensive consistency. S1083. Generate an evaluation matrix based on the comprehensive consistency and output the performance evaluation dataset; In another implementation, an axial index group is integrated from the calibrated optical axis parameters. This axial index group is then fused with a uniform spectrum derived from color temperature uniformity. A central axis framework is constructed using this uniform spectrum, resulting in a central axis distribution. A spectral consistency chain is determined based on this central axis distribution. The coordinate offset values ​​of the color coordinate deviations are obtained from this spectral consistency chain. The coordinate offset values ​​are used to quantify the consistency index set, resulting in the spectral consistency of the index set. The spectral consistency is used to evaluate the stable range of color rendering stability. A power spectral domain with uniform power distribution is fused from this stable range. The power spectral domain is used to determine the stability value of the color rendering stability, resulting in a comprehensive consistency of the stability value. An evaluation matrix is ​​generated based on this comprehensive consistency. A data cluster is constructed from the evaluation matrix, and a performance evaluation dataset is output for each data cluster.

[0093] Specifically, in one implementation, an axial index group is integrated from the calibrated optical axis parameters.

[0094] Specifically, the axial index group is obtained through parameter merging. A uniform spectrum is then generated by fusing the axial index group with uniform color temperature. This uniform spectrum represents the index and color temperature spectrally. A central axis framework is constructed using this uniform spectrum. The central axis framework refers to the process of forming a framework by superimposing spectra, such as layering spectra to construct the axial framework, resulting in the central axis distribution of the central axis framework. This distribution stabilizes the beam axis in LED lighting lenses. Further, a spectral consistency chain is determined based on the central axis distribution. The spectral consistency chain is a distributed chain structure. The coordinate offset values ​​of the color coordinate deviation are obtained from the spectral consistency chain. These coordinate offset values ​​are quantified values ​​of the deviation in the chain. A set of quantified consistency indicators is then generated based on these coordinate offset values. This quantification of the indicator set refers to the process of summarizing the offset values ​​into a set, such as summarizing and averaging the offsets to form a set, thus obtaining the spectral consistency of the indicator set. This consistency can quantify color coordinate deviation in headlight production.

[0095] Preferably, the stable range of color rendering stability is evaluated through spectral consistency. The stable range is a consistent range within which a power spectral domain with uniform power distribution is integrated. The power spectral domain is a spectral representation of the power within the range. The stability value of color rendering stability is determined using the power spectral domain. Determining the stability value involves averaging the spectral domain values; for example, averaging involves calculating the mean of spectral points to obtain the stability value, resulting in a comprehensive consistency of the stability values. This consistency is used to evaluate the color rendering effect in lighting scenarios.

[0096] For example, an evaluation matrix is ​​generated based on the overall consistency. The evaluation matrix is ​​a matrix-based representation of the consistency. Data clusters are constructed from the evaluation matrix to build the evaluation dataset. Each data cluster is a set of grouped clusters of the matrix. The performance evaluation dataset is then output for each data cluster.

[0097] In one possible implementation, the above process can be applied to multi-LED array lenses, integrating axial index groups while processing group data, fusing uniform spectra to construct a framework supporting multiple distributions, determining spectral consistency chains, obtaining offset values, quantizing index sets to evaluate stability intervals, fusing spectral domains to determine stability, generating matrices to construct data clusters, and outputting datasets to achieve spectral evaluation. Further...

[0098] It should be noted that the process of determining the stability value can be extended to include the average of the power spectral domain. When calculating the average of the spectral domain points, a weighted average is used to highlight the key spectrum and ensure that the overall consistency accurately reflects the stability.

[0099] Please see Figure 5 This application also provides a real-time analysis system 10 for the status of production equipment based on edge computing, the system comprising: The first generation module 11 is used to obtain the position information of the LED chip mounting process. The XY coordinate offset and rotation angle offset of the chip relative to the substrate are obtained in real time through the machine vision system, and the mounting position deviation distribution dataset is generated. The conversion module 12 is used to extract the offset angle and displacement vector of the LED chip from the mounting position deviation distribution dataset, and to convert the offset trajectory of the beam center axis from the chip offset angle and displacement vector according to the chip packaging structure and light source geometry. The first determining module 13 is used to analyze the dynamic change trend of the beam center axis based on the offset trajectory of the beam center axis, and determine the spatial distribution characteristics of the beam axis based on the dynamic change trend of the beam center axis in combination with the SIC method of LED array arrangement. The second generation module 14 is used to identify optical axis offset patterns and abnormal offset points through the spatial distribution characteristics of the beam axis, calibrate the real-time adjustment angle and displacement of the lens optical axis according to the identified offset pattern, and generate a lens calibration instruction set based on the adjustment angle and displacement. The calibration module 15 is used to dynamically adjust the position of the lens optical axis according to the optical axis offset amplitude and direction if the adjustment angle in the lens calibration instruction set exceeds the preset angle threshold, to compensate for the beam axis offset in real time, and to obtain the calibrated optical axis parameters. The judgment module 16 is used to analyze the propagation path of light in the lens through the calibrated optical axis parameters, perform ray tracing simulation processing on the propagation path, generate a light distribution dataset, and judge the uniformity of color temperature distribution based on the luminous spectrum characteristics of the LED chip. The second determining module 17 is used to determine the position of the target lens based on the uniformity of color temperature distribution and by adjusting the lens position attribute. Evaluation module 18 is used to determine the spectral consistency by determining a stable beam center axis distribution based on the calibrated optical axis parameters and color temperature distribution uniformity, and to obtain a performance evaluation dataset through comprehensive evaluation.

[0100] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for real-time analysis of the status of production equipment based on edge computing, characterized in that, The method includes: The position information of the LED chip mounting process is obtained, and the XY coordinate offset and rotation angle offset of the chip relative to the substrate are obtained in real time through the machine vision system, generating a mounting position deviation distribution dataset. The offset angle and displacement vector of the LED chip are extracted from the mounting position deviation distribution dataset. Based on the chip packaging structure and light source geometry, the offset trajectory of the beam center axis is calculated from the chip offset angle and displacement vector. The dynamic change trend of the beam center axis is analyzed based on the offset trajectory of the beam center axis, and the spatial distribution characteristics of the beam axis are determined by the dynamic change trend of the beam center axis in combination with the SIC arrangement of the LED array. The optical axis offset mode and abnormal offset point are identified by the spatial distribution characteristics of the beam axis. The real-time adjustment angle and displacement of the lens optical axis are calibrated according to the identified offset mode. A lens calibration instruction set is generated based on the adjustment angle and displacement. If the adjustment angle in the lens calibration instruction set exceeds the preset angle threshold, the lens optical axis position is dynamically adjusted according to the optical axis offset amplitude and direction to compensate for the beam axis offset in real time and obtain the calibrated optical axis parameters. The propagation path of light in the lens is analyzed by calibrating the optical axis parameters, and the propagation path is simulated by ray tracing to generate a light distribution dataset. The uniformity of color temperature distribution is judged based on the emission spectrum characteristics of the LED chip. The position of the target lens is determined by adjusting the lens position attribute based on the uniformity of color temperature distribution. Based on the calibrated optical axis parameters and color temperature distribution uniformity, a stable beam center axis distribution is determined to ensure spectral consistency, and a comprehensive evaluation dataset is obtained.

2. The method for real-time analysis of production equipment status based on edge computing according to claim 1, characterized in that, The acquisition of position information during the LED chip mounting process involves real-time monitoring using a machine vision system to obtain the XY coordinate offset and rotation angle offset of the chip relative to the substrate during the mounting process, generating a mounting position deviation distribution dataset, including: The machine vision system acquires images of the chip position to generate raw pixel data. Based on this raw pixel data, an edge detection algorithm is used to determine the chip edge contour, obtaining the center coordinates of the chip edge contour. The center coordinates are compared with a substrate reference point to calculate the torsional difference of the rotation angle and the vector components of the coordinate offset. Classification labels are generated based on the torsional difference and the vector components. Historical sequences of angle deviations are integrated using these classification labels to construct a time-axis correlation matrix. Clustering is performed on the time-axis correlation matrix to generate statistical features of the cluster groups. These statistical features are then fused with the dynamic path of the mounting process to generate a multi-dimensional deviation mapping, outputting the mounting position deviation distribution dataset.

3. The method for real-time analysis of production equipment status based on edge computing according to claim 1, characterized in that, The step of extracting the offset angle and displacement vector of the LED chip from the mounting position deviation distribution dataset, and calculating the offset trajectory of the beam center axis from the chip offset angle and displacement vector based on the chip packaging structure and light source geometry, includes: The initial angle value of the offset angle and the displacement component of the displacement vector are extracted from the mounting position deviation distribution dataset to construct vector-angle pairs and generate a composite deviation set. The chip casing parameters of the chip package structure are analyzed based on the composite deviation set, and the curvature radius data of the light source geometry are fused to generate a structural fusion body. The package geometry is mapped through the structural fusion body to determine the initial offset path of the beam axis. The path nodes are adjusted according to the initial offset path to generate a corrected offset path. The conversion factor is processed according to the corrected offset path to generate an axial sequence. The trajectory is smoothed according to the axial sequence to generate a continuous trajectory line. The offset trajectory of the beam center axis is determined based on the continuous trajectory line and the deviation data filtering threshold.

4. The method for real-time analysis of production equipment status based on edge computing according to claim 1, characterized in that, The step of analyzing the dynamic change trend of the beam center axis based on the offset trajectory of the beam center axis, and determining the spatial distribution characteristics of the beam axis from the dynamic change trend of the beam center axis by combining the LED array arrangement SIC method, includes: A trend sequence is extracted from the offset trajectory of the beam center axis to construct a trend variation map; the LED array coordinates are fused according to the trend variation map to generate a layout grid; the correlation chain of the offset trend is integrated according to the layout grid to generate a fused trend body; the axial interval is quantified according to the fused trend body to determine the radial dispersion; the angular tilt distribution is generated according to the radial dispersion distribution angle tilt vector; and characteristic clusters are generated according to the angular tilt distribution to determine the spatial distribution characteristics of the beam axis.

5. The method for real-time analysis of production equipment status based on edge computing according to claim 1, characterized in that, The process involves identifying optical axis offset modes and abnormal offset points based on the spatial distribution characteristics of the beam axis, calibrating the real-time adjustment angle and displacement of the lens optical axis according to the identified offset modes, and generating a lens calibration instruction set based on the adjustment angle and displacement, including: By identifying distribution clusters based on the spatial distribution characteristics of the beam axis, matching mode templates, and generating isolated nodes of abnormal offset points; by filtering noise thresholds based on the isolated nodes, generating classification labels for offset modes; by adjusting angle increments based on the classification labels, fusing dynamic weights, and generating a calibration angle set; by quantizing displacement vectors based on the calibration angle set, generating a calibration displacement set; and by generating an instruction sequence based on the calibration displacement set and mode parameters, outputting a lens calibration instruction set.

6. The method for real-time analysis of production equipment status based on edge computing according to claim 1, characterized in that, If the adjustment angle in the lens calibration command set exceeds a preset angle threshold, the lens optical axis position is dynamically adjusted according to the optical axis offset amplitude and direction to compensate for the beam axis offset in real time, thereby obtaining the calibrated optical axis parameters, including: The adjustment angle value is obtained from the lens calibration instruction set, and the threshold state is determined. The offset processing is activated according to the threshold state to generate an activated offset set. The amplitude spectrum is evaluated according to the activated offset set to generate a direction vector. The compensation chain is fused according to the direction vector to generate a dynamic compensation body. The position nodes are adjusted according to the dynamic compensation body to generate a compensated optical axis position. The stability index is fused according to the compensated optical axis position to generate the calibrated optical axis parameters.

7. The method for real-time analysis of production equipment status based on edge computing according to claim 1, characterized in that, The process involves analyzing the propagation path of light in the lens using calibrated optical axis parameters, performing ray tracing simulation on the propagation path to generate a light distribution dataset, and determining the color temperature distribution uniformity based on the luminous spectrum characteristics of the LED chip. This includes: The axial parameter set is analyzed from the calibrated optical axis parameters to construct the light propagation chain and generate the propagation path; the simulation field is simulated according to the propagation path to generate the simulation path; a distribution lattice is generated according to the simulation path, spectral lines are mapped, and the light distribution dataset is generated; the emission spectrum is fused according to the light distribution dataset to evaluate the color temperature distribution and generate the color temperature distribution uniformity.

8. The method for real-time analysis of production equipment status based on edge computing according to claim 1, characterized in that, After generating the light distribution dataset, the following is included: The emission spectrum is fused based on the light distribution dataset to generate a deviation range; the color temperature distribution is evaluated based on the deviation range to generate a uniformity index; a uniformity threshold is determined based on the uniformity index to generate color temperature distribution uniformity; a deviation description is determined based on the color temperature distribution uniformity, and the deviation value of the color temperature distribution uniformity is output.

9. The method for real-time analysis of production equipment status based on edge computing according to claim 1, characterized in that, The process involves determining spectral consistency by establishing a stable beam center axis distribution based on calibrated optical axis parameters and color temperature distribution uniformity, and then comprehensively evaluating the results to obtain a performance evaluation dataset, including: The axial index group is integrated from the calibrated optical axis parameters, and the uniform spectrum of the color temperature distribution uniformity is fused to generate a central axis distribution; a spectral consistency chain is determined based on the central axis distribution to generate spectral consistency; the stable interval is evaluated based on the spectral consistency, and the power spectral domain is fused to generate comprehensive consistency; an evaluation matrix is ​​generated based on the comprehensive consistency, and the performance evaluation dataset is output.

10. A real-time status analysis system for production equipment based on edge computing, characterized in that, The system includes: The first generation module is used to obtain the position information of the LED chip mounting process. The XY coordinate offset and rotation angle offset of the chip relative to the substrate are obtained in real time through the machine vision system, and the mounting position deviation distribution dataset is generated. The conversion module is used to extract the offset angle and displacement vector of the LED chip from the mounting position deviation distribution dataset, and to convert the offset trajectory of the beam center axis from the chip offset angle and displacement vector according to the chip packaging structure and light source geometry. The first determining module is used to analyze the dynamic change trend of the beam center axis based on the offset trajectory of the beam center axis, and determine the spatial distribution characteristics of the beam axis from the dynamic change trend of the beam center axis in combination with the SIC method of LED array arrangement. The second generation module is used to identify optical axis offset patterns and abnormal offset points through the spatial distribution characteristics of the beam axis, calibrate the real-time adjustment angle and displacement of the lens optical axis according to the identified offset patterns, and generate a lens calibration instruction set based on the adjustment angle and displacement. The calibration module is used to dynamically adjust the position of the lens optical axis according to the optical axis offset amplitude and direction if the adjustment angle in the lens calibration instruction set exceeds the preset angle threshold, to compensate for the beam axis offset in real time, and obtain the calibrated optical axis parameters. The judgment module is used to analyze the propagation path of light in the lens through the calibrated optical axis parameters, perform ray tracing simulation processing on the propagation path, generate a light distribution dataset, and judge the uniformity of color temperature distribution based on the emission spectrum characteristics of the LED chip. The second determining module is used to determine the position of the target lens based on the uniformity of color temperature distribution and by adjusting the lens position attribute. The evaluation module is used to determine the stable beam center axis distribution and spectral consistency based on the calibrated optical axis parameters and color temperature distribution uniformity, and to obtain a performance evaluation dataset through comprehensive evaluation.

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