CN-121982059-A - Intelligent control method and system for bottom shell laminating equipment

CN121982059ACN 121982059 ACN121982059 ACN 121982059ACN-121982059-A

Abstract

The invention relates to the technical field of industrial automation and discloses an intelligent control method and system of bottom shell laminating equipment, wherein the method comprises the steps of capturing a bottom shell image through a camera and preprocessing to obtain a clear image; extracting an edge contour point set, ensuring the integrity of the contour through continuity verification, calculating surface characteristic parameters based on curvature analysis, optimizing a data set, generating a preliminary motion track through curve fitting by utilizing the optimized data set, performing control point adjustment and smooth optimization on a deviation overrun region, extracting a track key point sequence, monitoring the position matching degree of a swing arm in real time and generating dynamic adjustment parameters, updating a control instruction or backtracking according to the matching result, recalculating the characteristic parameters, and finally generating an optimal arc motion track covering all edge points. The method can realize high-precision and self-adaptive control of the lamination of the bottom shell under the complex light and shape, and remarkably improves lamination quality and production efficiency.

Inventors

WANG YAN
HU SHIKAI
ZHOU LIANG

Assignees

深圳市富越机电设备有限公司

Dates

Publication Date: 20260505
Application Date: 20251219

Claims (9)

1. An intelligent control method of bottom shell laminating equipment is characterized by comprising the following steps: capturing bottom shell image data through a camera, and preprocessing to obtain a clear bottom shell image; According to the clear bottom shell image, combining edge detection, extracting an edge contour point set, calculating the continuity of the edge contour point set, determining that the contour is complete if the continuity meets a preset continuity threshold, and performing image enhancement processing if the continuity does not meet the preset continuity threshold to obtain a complete edge contour point set; Calculating surface characteristic parameters through curvature analysis according to the complete edge contour point set, and if the surface characteristic parameters meet a preset fitting requirement threshold, clustering and grouping optimization are carried out to obtain an optimized surface characteristic data set; Performing curve fitting on the optimized surface characteristic data set to generate a preliminary arc motion trail, calculating trail deviation of the preliminary arc motion trail, and if the trail deviation exceeds a preset trail deviation threshold, adjusting curve control points and combining the preliminary arc motion trail to perform optimization to obtain a smooth arc motion trail; Extracting a key contact point sequence from the smooth arc motion track, acquiring current position coordinates of equipment in real time, calculating track matching degree according to the key contact point sequence and the current position coordinates, and generating dynamic adjustment parameters according to the track matching degree; Calculating updated track matching degree according to the control instruction of the dynamic adjustment parameter updating device, if the updated track matching degree is higher than the track matching degree, confirming that the instruction is effective, otherwise, performing filtering processing on the current position coordinates, and correcting the dynamic adjustment parameters to obtain a corrected dynamic adjustment parameter set; And updating the equipment control instruction again according to the corrected dynamic adjustment parameter set to generate a final lamination execution sequence, and adjusting the final lamination execution sequence by combining real-time operation data collected in real time to obtain an optimal arc movement track.
2. The intelligent control method of the bottom shell laminating device according to claim 1, wherein capturing bottom shell image data by a camera and preprocessing the bottom shell image data to obtain a clear bottom shell image comprises: Capturing original image data containing noise and reflection interference through a high-resolution camera, and adjusting exposure parameters according to complex light conditions to obtain a first image; performing median filtering processing on the first image to obtain a second image; Aiming at the reflection interference in the second image, adopting polarization filtering to analyze pixel brightness distribution, and if the analysis result determines that the reflection interference exists, adjusting a brightness value to obtain a third image; And carrying out histogram equalization processing on the third image to adjust pixel gray level distribution, and improving contrast ratio to obtain a clear bottom shell image.
3. The intelligent control method of a bottom shell laminating device according to claim 1, wherein the steps of extracting an edge contour point set according to the clear bottom shell image in combination with edge detection, calculating continuity of the edge contour point set, determining that the contour is complete if the continuity meets a preset continuity threshold, and performing image enhancement processing if the continuity does not meet the preset continuity threshold to obtain a complete edge contour point set include: Processing gray distribution of the clear bottom shell image by adopting a Canny edge detection algorithm, and identifying edge pixels through gradient calculation to obtain an edge contour point set; performing continuity calculation on the edge contour point set, and adopting eight-neighborhood connection analysis to count the connection quantity of adjacent pixels to obtain continuity; If the continuity meets a preset continuity threshold, determining that the contour is complete, and if the continuity does not meet the preset continuity threshold, performing image enhancement processing to obtain a complete edge contour point set.
4. The intelligent control method of the bottom shell laminating equipment according to claim 1, wherein the calculating the surface characteristic parameter through curvature analysis according to the complete edge contour point set, and if the surface characteristic parameter meets a preset laminating requirement threshold, performing clustering grouping optimization to obtain an optimized surface characteristic data set comprises: Calculating surface characteristic parameters through curvature analysis according to the complete edge contour point set; Screening data of the surface characteristic parameters meeting a preset bonding requirement threshold value to obtain a data set meeting the bonding requirement; And clustering and grouping optimization is carried out on the data sets meeting the fitting requirements by adopting K-means clustering, and an optimized surface characteristic data set is generated.
5. The intelligent control method of the bottom shell laminating device according to claim 1, wherein the curve fitting is performed on the optimized surface feature data set to generate a preliminary arc motion track, track deviation of the preliminary arc motion track is calculated, if the track deviation exceeds a preset track deviation threshold, curve control points are adjusted and the preliminary arc motion track is combined for optimization, so that a smooth arc motion track is obtained, and the method comprises the following steps: Extracting the coordinates of the boundary point set of the complex shape region from the optimized surface feature data set through geometric feature analysis, and processing the coordinates of the boundary point set by adopting a spline interpolation method to generate a first arc point set; performing curve fitting processing on the first arc point set to generate a preliminary arc motion track, and calculating track deviation by comparing the preliminary arc motion track with the coordinates of the boundary point set; If the track deviation exceeds a preset track deviation threshold, gradient descent is adopted to adjust curve control points, and a second arc track is generated; and processing the coordinate point set of the second arc track through Gaussian filtering, optimizing track smoothness, and generating a smooth arc motion track.
6. The intelligent control method of the bottom shell laminating device according to claim 1, wherein the extracting a key contact point sequence from the smooth arc motion track, acquiring current position coordinates of the device in real time, calculating a track matching degree according to the key contact point sequence and the current position coordinates, and generating a dynamic adjustment parameter according to the track matching degree comprises: extracting key contact points from the point set coordinates of the smooth arc motion track to generate a key contact point sequence; acquiring current position coordinates of equipment in real time, and calculating track matching degree according to the key contact point sequence and the current position coordinates; And if the track matching degree is lower than a preset track matching threshold, the motion control parameters of the swing arm are adjusted in real time through PID control, so that dynamic adjustment parameters are obtained.
7. The intelligent control method of a bottom shell laminating apparatus according to claim 6, wherein the updating the apparatus control command according to the dynamic adjustment parameter calculates an updated track matching degree, if the updated track matching degree is higher than the track matching degree, the command is confirmed to be valid, otherwise, the current position coordinate is filtered, and the dynamic adjustment parameter is corrected, so as to obtain a corrected dynamic adjustment parameter set, including: According to the dynamic adjustment parameter updating equipment control instruction, calculating the updating track matching degree; if the updated track matching degree is higher than the track matching degree, confirming that the instruction is effective, otherwise, performing filtering processing on the current position coordinates to obtain a noise reduction position coordinate set; Calculating Euclidean distance between the noise reduction position coordinate set and the key contact point sequence to obtain a track matching degree deviation data set; if the value in the track matching degree deviation data set is lower than a preset track matching degree deviation threshold value, extracting surface characteristic parameters through curvature analysis to generate a correction parameter set; and updating the equipment control instruction according to the correction parameter set to obtain a correction dynamic adjustment parameter set.
8. The intelligent control method of the bottom shell laminating equipment according to claim 1, wherein the step of updating the equipment control command again according to the modified dynamic adjustment parameter set to generate a final laminating execution sequence, and adjusting the final laminating execution sequence to obtain an optimal arc motion track by combining real-time operation data collected in real time, comprises the steps of: Updating the equipment control instruction again according to the corrected dynamic adjustment parameter set to generate a final fitting execution sequence; And if the final fitting execution sequence is not completely covered, supplementing control points into the final fitting execution sequence and reconstructing the track until complete coverage is realized, thereby obtaining the optimal arc motion track.
9. An intelligent control system of drain pan laminating equipment, its characterized in that includes: the image preprocessing module is used for capturing bottom shell image data through the camera and preprocessing the bottom shell image data to obtain a clear bottom shell image; The contour extraction module is used for extracting an edge contour point set according to the clear bottom shell image and combining edge detection, calculating the continuity of the edge contour point set, determining that the contour is complete if the continuity accords with a preset continuity threshold value, and performing image enhancement processing if the continuity does not accord with the preset continuity threshold value to obtain a complete edge contour point set; the feature optimization module is used for calculating surface feature parameters through curvature analysis according to the complete edge contour point set, and if the surface feature parameters accord with a preset fitting requirement threshold, clustering grouping optimization is carried out to obtain an optimized surface feature data set; The track generation module is used for performing curve fitting on the optimized surface characteristic data set, generating a preliminary arc movement track, calculating track deviation of the preliminary arc movement track, and if the track deviation exceeds a preset track deviation threshold, adjusting curve control points and combining the preliminary arc movement track to perform optimization to obtain a smooth arc movement track; The real-time monitoring module is used for extracting a key contact point sequence from the smooth arc motion track, acquiring the current position coordinate of the equipment in real time, calculating track matching degree according to the key contact point sequence and the current position coordinate, and generating dynamic adjustment parameters according to the track matching degree; The instruction updating module is used for updating the equipment control instruction according to the dynamic adjustment parameters, calculating updated track matching degree, if the updated track matching degree is higher than the track matching degree, confirming that the instruction is valid, otherwise, performing filtering processing on the current position coordinates, and correcting the dynamic adjustment parameters to obtain a corrected dynamic adjustment parameter set; And the system integration module is used for updating the equipment control instruction again according to the corrected dynamic adjustment parameter set to generate a final lamination execution sequence, and adjusting the final lamination execution sequence by combining real-time operation data collected in real time to obtain an optimal arc motion track.

Description

Intelligent control method and system for bottom shell laminating equipment Technical Field The invention relates to the technical field of industrial automation, in particular to an intelligent control method and system of bottom shell laminating equipment. Background At present, in the modern manufacturing industry, a bottom shell attaching technology is a key link for ensuring the tightness and structural stability of a product, and is widely applied to the fields of electronic equipment, automobile parts and the like, and the precision of the bottom shell attaching technology directly influences the quality and the service life of the product. With the advancement of intelligent manufacturing, automation equipment requires greater flexibility and accuracy to accommodate the diverse production needs. Especially when facing the bottom shell with complex curved surfaces, irregular edges or special materials, the traditional bonding method is difficult to ensure bonding precision and efficiency at the same time, and becomes a technical bottleneck for restricting the quality improvement of products. In the prior art, the bottom shell is attached through a preprogrammed mechanical arm movement path, and the method is operated based on a fixed track and cannot be dynamically adjusted according to actual geometric features of the bottom shell. In practical application, because the shape of the bottom shell has manufacturing tolerance, assembly error or complex and changeable design, the preset track often has deviation from the outline of a real object, especially when the shape of the bottom shell faces to an irregular geometric shape, the equipment lacks real-time sensing and self-adaptive capacity, and is difficult to accurately identify the edge outline and the fine surface characteristics, so that the problems of loose lamination, dislocation or excessive stretching of materials and the like occur in the laminating process, and the problems of frequent shutdown, manual parameter adjustment or special die replacement are required, so that the production efficiency and the product consistency are seriously affected. When the existing attaching scheme is used for coping with complex geometric shapes, due to the lack of the capability of real-time sensing and dynamic track generation of the actual contour of the bottom shell, closed-loop control and online optimization of the attaching process cannot be realized. Therefore, the prior art has the problem of low fitting precision caused by the inability to dynamically adapt to the shape of the complex bottom shell and the lack of the capability of real-time sensing and track adjustment. Disclosure of Invention The invention provides an intelligent control method and system of bottom shell laminating equipment, which are used for solving the problem of low bottom shell laminating precision. In order to solve the above technical problems, the present invention provides an intelligent control method for a bottom shell laminating apparatus, including: capturing bottom shell image data through a camera, and preprocessing to obtain a clear bottom shell image; According to the clear bottom shell image, combining edge detection, extracting an edge contour point set, calculating the continuity of the edge contour point set, determining that the contour is complete if the continuity meets a preset continuity threshold, and performing image enhancement processing if the continuity does not meet the preset continuity threshold to obtain a complete edge contour point set; Calculating surface characteristic parameters through curvature analysis according to the complete edge contour point set, and if the surface characteristic parameters meet a preset fitting requirement threshold, clustering and grouping optimization are carried out to obtain an optimized surface characteristic data set; Performing curve fitting on the optimized surface characteristic data set to generate a preliminary arc motion trail, calculating trail deviation of the preliminary arc motion trail, and if the trail deviation exceeds a preset trail deviation threshold, adjusting curve control points and combining the preliminary arc motion trail to perform optimization to obtain a smooth arc motion trail; Extracting a key contact point sequence from the smooth arc motion track, acquiring current position coordinates of equipment in real time, calculating track matching degree according to the key contact point sequence and the current position coordinates, and generating dynamic adjustment parameters according to the track matching degree; Calculating updated track matching degree according to the control instruction of the dynamic adjustment parameter updating device, if the updated track matching degree is higher than the track matching degree, confirming that the instruction is effective, otherwise, performing filtering processing on the current position coordinates, and correcting the dynamic adjustment