PLC labeling machine control method and system based on fitting site cruising

Abstract

The invention relates to the technical field of labeling equipment control, in particular to a PLC labeling machine control method and system based on fitting site cruising. The pose time sequence state variable corresponding to the tracking pose change of the industrial vision camera is described on the basis of the pre-integration relativity of the inertia measurement data to the continuous image data, and the ideal cruise pose of the labeling mechanism in cruise attachment relative to the optimal attachment site on the target machined object is obtained; if the PLC labeling machine is incompatible with automatic cruise planning on the special-shaped surface on the control premise of an ideal cruise pose, mapping a cruise mapping distance matrix from labeling to a specified labeling processing area when the ideal cruise pose is reached in a space coordinate domain based on an operation transformation track; a labeling mechanism is controlled according to semi-variance Gaussian model identification solution of the cruise mapping distance matrix; according to the invention, accurate label lamination control can be carried out on the PLC labeling machine based on the lamination site cruise of the special-shaped surface, so that the labeling quality of the PLC labeling machine on the special-shaped surface processed object is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of labeling equipment control, and in particular to a PLC labeling machine control method and system based on fitting site cruising. Background Art

[0002] With the continuous development of automation technology, labeling machines have been widely used in various industries, especially in the rapid identification and packaging of goods in production lines. The key function of labeling machines is to accurately attach labels to the surface of objects, and with the changes in product types and production methods, the technical requirements of labeling machines are also constantly improving. Traditional labeling machines mainly rely on mechanical control, photoelectric sensing, position sensing and other methods to perform labeling positioning, but due to factors such as irregular product surfaces and different label shapes, especially the labeling requirements for special-shaped surfaces in special processing, it is easy to cause label misalignment, offset, overlap and other phenomena, affecting production efficiency and product quality. At the same time, the existing labeling processing of special-shaped surfaces usually uses the rotation or movement of the product itself to wrap the static label around and fit it to the specified area, but this PLC labeling machine is easy to deviate from the specified labeling area with the dynamic movement of the product itself, making it impossible for the label to be accurately aligned and fitted to the specified labeling area, and it will also produce a large number of fitting bubbles, fitting wrinkles and loose adhesion, which greatly affects the labeling quality of the special-shaped surface processing. Therefore, it is necessary to develop a control method that can enable the PLC labeling machine to meet the autonomous position cruising and bonding during the dynamic labeling process of the special-shaped surface to solve the above problems. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a PLC labeling machine control method and system based on fitting site cruising.

[0004] To achieve the above object, the technical solution adopted by the present invention is: The first aspect of the present invention provides a PLC labeling machine control method based on laminating site cruising, comprising the following steps: S102: Acquire continuous image data of the labeling conveyance of the target object, define the best fitting position array of the target object according to the specification parameter information of the labeling label and the curvature of the labeling area surface of the target object, and linearly track the optical flow motion of the continuous image data based on the sliding window neighborhood preset in the best fitting position array to obtain the operation transformation trajectory of the best fitting position on the target object within a preset time period; S104: Acquire the tracking posture change of the industrial vision camera through the industrial vision camera position tracking the operation transformation trajectory, and acquire inertial measurement data, describe the posture time series state variables of the tracking posture change based on the pre-integration relativity of the inertial measurement data to the continuous image data, and obtain the ideal cruising posture of the labeling mechanism cruising and fitting relative to the best fitting position on the target workpiece under the operation transformation trajectory condition; S106: If the cruise function mode equipped with the PLC labeling machine is not compatible with the automatic cruise planning of the operation transformation trajectory corresponding to the profiled surface based on the ideal cruise posture as the control premise, then based on the cruise mapping distance matrix for labeling to the specified labeling processing area in the operation transformation trajectory mapping space coordinate domain, the labeling mechanism is controlled according to the semi-variance Gaussian model identification and solution of the cruise mapping distance matrix; S108: If the defective fitting rate of the labeling head for the labeling label is higher than the minimum defective fitting rate, the unreasonable sub-fitting site distribution field of the labeling label is extracted according to the criterion that the stickiness index exceeds the unqualified stickiness index, and the unreasonable sub-fitting site distribution field is screened for the best fitting site solution according to the expected fitting requirements of the labeling label to obtain the best fitting site matrix, and the fitting of the labeling head is controlled according to the best fitting site matrix.

[0005] More specifically, the step S102 includes the following steps: Obtaining a specified labeling processing area of ​​the processing and transportation mechanism, and photographing the target processing object that begins to enter the specified labeling processing area within a preset time period frame by frame through an industrial vision camera mounted on the labeling mechanism, so as to obtain continuous image data of the target processing object during the labeling processing operation; Obtaining specification parameter information of the labeling label, synchronously obtaining the labeling task of the target object and the surface curvature of the labeling area, and planning and defining the fitting site of the surface curvature of the labeling area based on the specification parameter information and the labeling task to obtain the optimal fitting site array of the target object; A local sliding window neighborhood of pixels of the fitting sites is preset based on the matrix pattern expressed by the best fitting site array, and a Sobel operator is introduced to calculate the spatial gradient and the temporal gradient of every two adjacent frames of the continuous image data; The local sliding window area is divided into N sub-neighborhoods based on the number of best fitting sites, and for each sub-neighborhood, the linear dynamic motion of each sub-neighborhood is described by the optical flow components of the best fitting site pixels in the horizontal direction and the vertical direction, and the spatial gradient and the temporal gradient tracking, to generate an optical flow equation for each sub-neighborhood; Solve the optical flow equation to obtain the optical flow solution of each best fitting point pixel in each sub-neighborhood, repeat the above steps of calculating the spatial gradient and the temporal gradient and tracking and solving the optical flow equation in the image frame until each frame of the continuous image data is calculated and the iteration is stopped; After the iteration stops, the optical flow solutions of all the best fitting points in the continuous image data are output, and the operation transformation trajectory of the best fitting point on the target processing object within a preset time period is determined based on the optical flow solutions of all the best fitting points.

[0006] More specifically, the step S104 includes the following steps: Tracking the operation transformation trajectory of the best fitting point on the target workpiece within the preset time period by an industrial vision camera to obtain the tracking posture change of the industrial vision camera cruising the best fitting point on the surface curvature of the labeling area; During the site tracking process, the inertial measurement unit carried on the labeling mechanism is used to collect inertial measurement data when the industrial visual camera runs the tracking posture change; wherein the inertial measurement data includes the angular velocity, linear acceleration and nonlinear acceleration of the gyroscope; Taking tracking posture changes as a constraint premise, the inertial measurement data is used to perform pre-integration calculations on the acceleration and angular velocity of the continuous image data during the labeling process of the target object to obtain a pre-integration result of the relative posture change, and the Kalman filter is used to perform a time-series state fusion of the tracking posture change and the pre-integration result to obtain a posture time-series state variable matrix; Extract the pre-integration error value of the inertial measurement data through the pre-integration result, depict the pose nodes based on the pose timing state variable matrix, define the state variable error constraints between each pose node based on the pre-integration error value, and construct the pose timing state model diagram according to the pose nodes and the state variable error constraints between each pose node; Taking the operation transformation trajectory as the target rule, presetting the square residual function according to the state variable error constraint, calculating the gradient of the square residual function with respect to the target rule, and generating the current approximate Hessian matrix of the posture timing state model diagram and the current gradient search direction according to the gradient; Preset the iteration depth threshold, perform linear iterative update on the state of each pose node in the current Hessian matrix through the current gradient search direction until the square residual function converges to the iteration depth threshold, and output the updated approximate Hessian matrix; The ideal cruising position of the labeling mechanism for cruising and fitting the best fitting position on the target object under the condition of the operation transformation trajectory is determined based on the state variable estimation of the updated approximate Hessian matrix.

[0007] More specifically, the step S106 includes the following steps: Acquire functional mode information of the PLC labeling machine, and determine whether the autonomous cruise function equipped by the PLC labeling machine is compatible with the automated cruise planning of the corresponding operation change trajectory of the special-shaped surface based on the ideal cruise posture as the control premise according to the functional mode information; If the cruise function mode equipped with the PLC labeling machine is not compatible with the automatic cruise planning of the corresponding operation change trajectory of the special-shaped surface based on the ideal cruise posture as the control premise, then the initial design drawing of the labeling mechanism is obtained; Based on the initial design draft, the predetermined degrees of freedom and the maximum spatial operating range of the labeling mechanism are obtained, the spatial coordinates of the predetermined degrees of freedom are constructed by assigning the maximum spatial operating range as a boundary frame, and a spatial coordinate domain for the labeling mechanism to achieve the predetermined degrees of freedom is generated; Obtaining a discrete spatial coordinate array of the labeling mechanism to achieve an ideal cruising posture for labeling through a spatial coordinate domain, which is defined as a first discretized spatial coordinate array, and obtaining a discrete spatial coordinate array when the target object is located in a specified labeling processing area, which is defined as a second discretized spatial coordinate array; Based on the preset mapping clues of the operation transformation trajectory, the first discretized space coordinate array is cruise mapped to the second discretized space coordinate array according to the mapping clues as the target reference, and the Manhattan distance of each discrete space coordinate pair is continuously calculated one by one during the cruise mapping process to obtain a cruise mapping distance matrix; According to the cruise mapping distance matrix, a semi-variance Gaussian model of each discrete spatial coordinate pair between the first discretized spatial coordinate array and the second discretized spatial coordinate array is constructed, and the weighted coefficient of the corresponding interpolation point of each discrete spatial coordinate pair is solved by the spatial correlation index identified by the semi-variance Gaussian model. The cruise operation contour map is interpolated based on the weighted coefficient, and the labeling mechanism is controlled according to the cruise operation contour map.

[0008] More specifically, the method constructs a semi-variance Gaussian model of each discrete spatial coordinate pair between the first discretized spatial coordinate array and the second discretized spatial coordinate array according to the cruise mapping distance matrix, solves the weighted coefficient of the corresponding interpolation point of each discrete spatial coordinate pair through the spatial correlation index identified by the semi-variance Gaussian model, draws a cruise operation contour map based on the weighted coefficient interpolation, and controls the labeling mechanism according to the cruise operation contour map, which specifically includes the following steps: Determine the semivariance of each discrete spatial coordinate pair between the first discretized spatial coordinate array and the second discretized spatial coordinate array according to the cruise mapping distance matrix, and obtain a plurality of semivariance functions; The maximum likelihood method is introduced to perform maximization calculation of exponential likelihood fitting on the several semivariance functions to obtain a series of semivariance parameters, and a semivariance Gaussian model is constructed according to the series of semivariance parameters; wherein the semivariance model parameters include the range and effective distance of spatial association; Based on the semivariance function, identification is performed in the semivariance Gaussian model to determine the spatial correlation index of the first discretized space coordinate array reaching the second discretized space coordinate array according to the operation transformation trajectory under the premise of the ideal cruise posture; According to the spatial correlation index, the labeling mechanism is described in the premise that the labeling label is in an ideal cruise posture according to the operation transformation trajectory to carry the labeling label to fit the target processing object. The cruise mapping interpolation equations are transformed into an augmented matrix by elimination and back-substitution to solve, and the weight coefficient of each discrete space coordinate to the corresponding point to be interpolated is obtained; A contour interpolation map of the labeling mechanism control is constructed based on the given degrees of freedom, and a contour map of the labeling mechanism cruising under the given degrees of freedom constraints is generated by interpolating the weighted coefficients of each discrete spatial coordinate corresponding to the point to be interpolated in the contour interpolation map one by one, and marked as a cruising operation contour map; According to the cruise operation contour map, the cruise control decision of the labeling mechanism for the best fitting position on the special-shaped surface of the target processing object is planned to obtain a cruise control strategy. Based on the cruise control strategy, the labeling mechanism is controlled to fit the target processing object according to the operation transformation trajectory under the premise that the labeling mechanism is in an ideal cruise position, and a labeling control scheme is obtained.

[0009] More specifically, the step S108 includes the following steps: Obtain the label fitting log of the PLC labeling machine, extract the fitting defective rate of the labeling head for the labeling label through the label fitting log, and if the fitting defective rate is higher than the minimum fitting defective rate, obtain the specified fitting position of the labeling label; Obtain the model and specification information of the labeling head, extract the basic grasping size of the grasping joint corresponding to each specified fitting site on the labeling head through the model and specification information, and preset the fitting site area marginal threshold based on the basic grasping size; Constructing a fitting site field for labeling labels, dividing the fitting site field into M sub-fields based on the number of specified fitting sites, and performing fitting site inclusion planning for each sub-field until a boundary constraint of a fitting site region marginal threshold is reached, thereby obtaining each sub-fitting site distribution field; Extract the stickiness index of each sub-sticking site distribution area of ​​the labeled label under the defective sticking rate through the label sticking log; if the stickiness index exceeds the unqualified stickiness index, extract the sub-sticking site distribution area corresponding to the stickiness index and mark it as an unreasonable sub-sticking site distribution area; Obtaining an expected fitting requirement of the labeling label, obtaining an expected adhesion index of the labeling label according to the expected fitting requirement, calculating a deviation of the adhesion index of the unreasonable sub-fitting site distribution area compared to the expected adhesion index, and obtaining an adhesion index deviation value; According to the distribution field of unreasonable sub-fitting sites, a solution space of fitting sites is constructed, and several fitting site solutions remaining in the solution space excluding the specified fitting sites are obtained. Each fitting site solution is evaluated based on the stickiness index deviation value to obtain a solution evaluation value, and only the fitting site solution corresponding to the maximum solution evaluation value is extracted and marked as the best fitting site solution; The optimal fitting site matrix is ​​constructed by obtaining the optimal fitting site solution of each unreasonable sub-fitting site distribution field. The grabbing site and fitting force of the labeling head for the labeling label are controlled according to the optimal fitting site matrix to improve the poor adhesion phenomenon.

[0010] The second aspect of the present invention provides a PLC labeling machine control system based on fitting site cruising, the PLC labeling machine control system includes a memory and a processor, the memory stores a PLC labeling machine control method program based on fitting site cruising, when the PLC labeling machine control method program is executed by the processor, any one of the steps of the PLC labeling machine control method is implemented.

[0011] The present invention solves the technical defects existing in the background technology, and the beneficial technical effects of the present invention are: The continuous image data of the target object labeling conveyance and the array of the best fitting sites are obtained, and the optical flow motion of the continuous image data is linearly tracked based on the preset sliding window neighborhood of the best fitting site array to obtain the operation transformation trajectory of the best fitting site on the target object within the preset time period; the operation transformation trajectory is tracked to obtain the tracking posture change and inertial measurement data of the industrial vision camera, and the posture time series state variables of the tracking posture change are described based on the pre-integration relativity of the inertial measurement data to the continuous image data to obtain the ideal cruising posture of the labeling mechanism relative to the best fitting site on the target object under the operation transformation trajectory condition; if the PLC labeling machine The equipped cruise function mode is not compatible with the automatic cruise planning of the operation transformation trajectory corresponding to the special-shaped surface based on the ideal cruise posture as the control premise. Based on the cruise mapping distance matrix of the operation transformation trajectory mapping space coordinate domain about labeling to the specified labeling processing area to achieve the ideal cruise posture, the labeling mechanism is controlled according to the semi-variance Gaussian model identification and solution of the cruise mapping distance matrix; if the labeling head has a defective rate of fitting the labeling label higher than the minimum defective rate, the unreasonable sub-fitting site distribution field is screened for the best fitting site solution according to the expected fitting requirements of the labeling label, and the best fitting site matrix is ​​obtained, and the labeling head is controlled according to the best fitting site matrix. The present invention can accurately control the label fitting of the PLC labeling machine based on the fitting site cruise of the special-shaped surface, thereby improving the labeling quality of the PLC labeling machine for the processed objects with special-shaped surfaces, and avoiding the problem of inaccurate label fitting of the existing PLC labeling machine facing the special-shaped surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0013] Figure 1 A first method flow chart of a PLC labeling machine control method based on fitting site cruising is shown; Figure 2 A second method flow chart of a PLC labeling machine control method based on fitting site cruising is shown; Figure 3 A system framework diagram of a PLC labeling machine control system based on fitting site cruising is shown. DETAILED DESCRIPTION

[0014] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0015] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0016] The first aspect of the present invention provides a PLC labeling machine control method based on fitting site cruising, such as Figure 1 As shown, the following steps are included: S102: Acquire continuous image data of the labeling conveyance of the target object, define the best fitting position array of the target object according to the specification parameter information of the labeling label and the curvature of the labeling area surface of the target object, and linearly track the optical flow motion of the continuous image data based on the sliding window neighborhood preset in the best fitting position array to obtain the operation transformation trajectory of the best fitting position on the target object within a preset time period; S104: Acquire the tracking posture change of the industrial vision camera through the industrial vision camera position tracking the operation transformation trajectory, and acquire inertial measurement data, describe the posture time series state variables of the tracking posture change based on the pre-integration relativity of the inertial measurement data to the continuous image data, and obtain the ideal cruising posture of the labeling mechanism cruising and fitting relative to the best fitting position on the target workpiece under the operation transformation trajectory condition; S106: If the cruise function mode equipped with the PLC labeling machine is not compatible with the automatic cruise planning of the operation transformation trajectory corresponding to the profiled surface based on the ideal cruise posture as the control premise, then based on the cruise mapping distance matrix for labeling to the specified labeling processing area in the operation transformation trajectory mapping space coordinate domain, the labeling mechanism is controlled according to the semi-variance Gaussian model identification and solution of the cruise mapping distance matrix; S108: If the defective fitting rate of the labeling head for the labeling label is higher than the minimum defective fitting rate, the unreasonable sub-fitting site distribution field of the labeling label is extracted according to the criterion that the stickiness index exceeds the unqualified stickiness index, and the unreasonable sub-fitting site distribution field is screened for the best fitting site solution according to the expected fitting requirements of the labeling label to obtain the best fitting site matrix, and the fitting of the labeling head is controlled according to the best fitting site matrix.

[0017] It should be noted that the PLC labeling machine of the present invention fixes the target workpiece through a clamping mechanism and realizes its own rotation or movement, so that the label paper that is continuously conveyed can be wound and attached during the dynamic operation of the rotation or movement.

[0018] More specifically, the step S102 is as follows: Figure 2 As shown, the specific steps include: S202: Obtaining a specified labeling processing area of ​​the processing and transportation mechanism, and photographing the target processing object that enters the specified labeling processing area within a preset time period frame by frame through an industrial vision camera mounted on the labeling mechanism, so as to obtain continuous image data of the target processing object during labeling processing operation; S204: Obtaining specification parameter information of the labeling label, synchronously obtaining the labeling task of the target object and the surface curvature of the labeling area, and planning and defining the fitting site of the surface curvature of the labeling area based on the specification parameter information and the labeling task to obtain the optimal fitting site array of the target object; S206: presetting a local sliding window neighborhood of pixels of the fitting site based on the matrix pattern expressed by the best fitting site array, and introducing a Sobel operator to calculate the spatial gradient and the temporal gradient of every two adjacent frames of the continuous image data; S208: Divide the local sliding window area into N sub-neighborhoods based on the number of best fitting sites, and for each sub-neighborhood, use the optical flow components of the best fitting site pixels in the horizontal and vertical directions and the spatial gradient and temporal gradient to track and describe the linear dynamic motion of each sub-neighborhood, and generate an optical flow equation for each sub-neighborhood; S210: Solving the optical flow equation to obtain the optical flow solution of each best fitting point pixel in each sub-neighborhood, repeating the above steps of calculating the spatial gradient and the temporal gradient and tracking and solving the optical flow equation in the image frame until each frame of the continuous image data is calculated and the iteration is stopped; S212: After the iteration stops, the optical flow solutions of all the best fitting points in the continuous image data are output, and the operation transformation trajectory of the best fitting point on the target processing object within a preset time period is determined according to the optical flow solutions of all the best fitting points.

[0019] It should be noted that some PLC labeling machines on the market mainly serve the needs of special-shaped labeling, such as labeling of cylindrical, elliptical or other irregular shaped surfaces; however, since some PLC labeling machines for special-shaped surface labeling rotate or move the orientation of the labeling area of ​​the workpiece so that the labeling mechanism fits the label to cover the special-shaped fitting surface as it moves, this may make it impossible for the control of such PLC labeling machines to fit the special-shaped fitting surface of the target workpiece to the maximum extent, resulting in the phenomenon that the label cannot be accurately fitted or the fitting quality is poor. Therefore, the labeling mechanism can be accurately positioned and fitted by capturing the operating transformation trajectory of the fitting site on the special-shaped fitting surface of the target workpiece. Therefore, this method starts to capture continuous image data of the target object rotating or moving in the specified labeling processing area when the target object enters the specified labeling processing area; due to the existence of certain fitting curvatures on different profiled surfaces, there are slight differences in the fitting sites on different target objects. Therefore, the fitting sites of the labeling area surface curvature of the target object are planned and defined according to the specification characteristics of the labeling label and the labeling task of the target object, so as to limit the array of the best fitting sites, so that different label sizes can fit more closely on objects with different profiled surface curvatures, thereby reducing the wrinkle or bubble rate. Then, the best fitting site is used as the positioning target to perform frame-by-frame pixel capture analysis in the collected continuous image data. This method captures the changes in the brightness of the pixels between the two frames of images in the horizontal, vertical and time dimensions by setting a local sliding window neighborhood tracking, that is, the optical flow components in the horizontal and vertical directions and the spatial gradient and time gradient tracking are used to describe the linear dynamic motion of the pixels, thereby revealing the optical flow changes of the linear dynamic motion of the pixels at the best fitting point in the temporal space, which can greatly improve the pixel capture accuracy of the object's motion. Compared with the traditional pixel temporal capture method, it is clearer and more reliable, and meets the cruise positioning accuracy of the PLC labeling machine for the fitting site of the special-shaped surface at certain high-speed labeling production rates. Finally, the operation transformation trajectory of the best fitting site can be quickly obtained by iteratively solving the generated optical flow equation.

[0020] It should be noted that, on the one hand, this method can plan reasonable and optimal fitting sites for different special-shaped surface curvatures according to the specification characteristics of the labeling label, so that the labeling label can ensure complete fitting on the special-shaped surface processing object to the greatest extent, thereby improving the label fitting quality; on the other hand, it can quickly locate the changing trajectory of the best fitting site of the special-shaped surface target processing object by estimating the optical flow change of the target pixel point of the image data in the time-series space, providing a reliable positioning basis for the subsequent PLC labeling machine to control the labeling mechanism to cruise the best fitting site for label fitting, thereby greatly improving the accuracy of special-shaped surface label fitting.

[0021] More specifically, the step S104 includes the following steps: Tracking the operation transformation trajectory of the best fitting point on the target workpiece within the preset time period by an industrial vision camera to obtain the tracking posture change of the industrial vision camera cruising the best fitting point on the surface curvature of the labeling area; During the site tracking process, the inertial measurement unit carried on the labeling mechanism is used to collect inertial measurement data when the industrial visual camera runs the tracking posture change; wherein the inertial measurement data includes the angular velocity, linear acceleration and nonlinear acceleration of the gyroscope; Taking tracking posture changes as a constraint premise, the inertial measurement data is used to perform pre-integration calculations on the acceleration and angular velocity of the continuous image data during the labeling process of the target object to obtain a pre-integration result of the relative posture change, and the Kalman filter is used to perform a time-series state fusion of the tracking posture change and the pre-integration result to obtain a posture time-series state variable matrix; Extract the pre-integration error value of the inertial measurement data through the pre-integration result, depict the pose nodes based on the pose timing state variable matrix, define the state variable error constraints between each pose node based on the pre-integration error value, and construct the pose timing state model diagram according to the pose nodes and the state variable error constraints between each pose node; Taking the operation transformation trajectory as the target rule, presetting the square residual function according to the state variable error constraint, calculating the gradient of the square residual function with respect to the target rule, and generating the current approximate Hessian matrix of the posture timing state model diagram and the current gradient search direction according to the gradient; Preset the iteration depth threshold, perform linear iterative update on the state of each pose node in the current Hessian matrix through the current gradient search direction until the square residual function converges to the iteration depth threshold, and output the updated approximate Hessian matrix; The ideal cruising position of the labeling mechanism for cruising and fitting the best fitting position on the target object under the condition of the operation transformation trajectory is determined based on the state variable estimation of the updated approximate Hessian matrix.

[0022] It should be noted that due to the particularity of the irregular surface and the need for the PLC labeling machine to label target objects with different curvatures according to different processing tasks, the labeling head of the existing PLC labeling machine for the irregular surface cannot make adaptive deformation according to different labeling curvatures to fully fit the irregular surface. In other words, the labeling head on the labeling mechanism is relatively fixed and difficult to adapt to the dynamic labeling of irregular surfaces with different curvatures. This results in the labeling mechanism being unable to fully fit the label to the irregular surface of the area where it needs to be fitted under the premise of labeling the target object, which makes the labeling mechanism have a large label fitting error for objects with irregular surfaces. In view of this, this method uses the data collected by the industrial vision camera and the inertial measurement unit commonly carried on the labeling mechanism for posture linkage analysis. First, the tracking posture change of the industrial vision camera position tracking operation transformation trajectory is obtained. The tracking posture change reflects the global fitting trajectory of the labeling label on the irregular surface of the target object, that is, the morphological interpretation of the curvature of the irregular surface. The tracking posture change is an important basis for controlling the labeling mechanism's cruising position fitting. Then, the inertial measurement data in the process of position tracking is synchronously acquired, that is, these inertial measurement data are some posture compensation data describing the special-shaped surface generated when the industrial visual camera tracks the position, which can reflect the labeling azimuth vector information that cannot be expressed by the visual data. Then, the motion of adjacent image frames is calculated by pre-integration of the inertial measurement data, that is, the position change of the camera is estimated, and the pre-integration result of the relative position change is obtained. Since the inertial measurement data can compensate for the position expression error existing in the tracking position change, the method uses the time-series state fusion tracking position change and the position time-series state variable matrix generated by the pre-integration result to describe the position time-series state variable of the pre-integration error, and draws a pose time-series state model diagram in the form of the position nodes expressing the tracking position change and the state variable error constraints between the pose nodes reflected in the inertial measurement data, which is convenient for eliminating and optimizing the position expression error, so that the subsequent labeling mechanism cruises and tracks the best fitting position of the special-shaped surface fitting azimuth vector control is faster and more accurate, so that the labeling mechanism can fully fit according to the curvature of the special-shaped surface.

[0023] It should be noted that the posture timing state model diagram is used to display the posture expression error status of the inertial measurement data compensation tracking posture change. Therefore, a second-order derivative matrix that follows the target rule is further constructed according to the state variable error constraint between the posture nodes, that is, the approximate Hessian matrix. The approximate Hessian matrix is ​​called the approximate Hessian matrix in Chinese. The approximate Hessian matrix is ​​continuously updated through the current gradient search direction reflected by the posture timing state model diagram, so as to minimize the posture error of the inertial measurement data compensation tracking posture change, so that the labeling posture control of the labeling mechanism can closely follow the operation transformation of the best fitting site on the special-shaped surface to track and fit the label, and further ensure that the labeling posture tends to be idealized, thereby greatly improving the fitting site cruising accuracy of the labeling mechanism when the PLC labeling machine is operating and labeling the special-shaped surface processed object, and the label fitting control accuracy based on the fitting site cruising.

[0024] More specifically, the step S106 includes the following steps: Acquire functional mode information of the PLC labeling machine, and determine whether the autonomous cruise function equipped by the PLC labeling machine is compatible with the automated cruise planning of the corresponding operation change trajectory of the special-shaped surface based on the ideal cruise posture as the control premise according to the functional mode information; If the cruise function mode equipped with the PLC labeling machine is not compatible with the automatic cruise planning of the corresponding operation change trajectory of the special-shaped surface based on the ideal cruise posture as the control premise, then the initial design drawing of the labeling mechanism is obtained; Based on the initial design draft, the predetermined degrees of freedom and the maximum spatial operating range of the labeling mechanism are obtained, the spatial coordinates of the predetermined degrees of freedom are constructed with the maximum spatial operating range as the boundary frame assignment, and the spatial coordinate domain of the labeling mechanism to achieve the predetermined degrees of freedom is generated; Obtaining a discrete spatial coordinate array of the labeling mechanism to achieve an ideal cruising posture for labeling through a spatial coordinate domain, which is defined as a first discretized spatial coordinate array, and obtaining a discrete spatial coordinate array when the target object is located in a specified labeling processing area, which is defined as a second discretized spatial coordinate array; Based on the preset mapping clues of the operation transformation trajectory, the first discretized space coordinate array is cruise mapped to the second discretized space coordinate array according to the mapping clues as the target reference, and the Manhattan distance of each discrete space coordinate pair is continuously calculated one by one during the cruise mapping process to obtain a cruise mapping distance matrix; According to the cruise mapping distance matrix, a semi-variance Gaussian model of each discrete spatial coordinate pair between the first discretized spatial coordinate array and the second discretized spatial coordinate array is constructed, and the weighted coefficient of the corresponding interpolation point of each discrete spatial coordinate pair is solved by the spatial correlation index identified by the semi-variance Gaussian model. The cruise operation contour map is interpolated based on the weighted coefficient, and the labeling mechanism is controlled according to the cruise operation contour map.

[0025] It should be noted that the existing PLC labeling machines are equipped with autonomous cruise function to meet the autonomous cruise labeling control of the target processed object, but the autonomous cruise function of most PLC labeling machines can only adapt to the labeling cruise decision of the target processed object with a plane shape. The autonomous cruise of the special-shaped surface cannot be realized, or there are situations such as loose bonding, many bonding bubbles and bonding wrinkles, and it is difficult to perform automated cruise planning based on the ideal cruise posture and operation transformation trajectory, which makes the production quality of the special-shaped surface labeling of the target processed object poor, increases the rework rate and labeling cost, so it is necessary to solve the above pain points by making cruise control decisions for the labeling of the special-shaped surface. Therefore, this method defines the spatial coordinate domain of the labeling mechanism based on the established degrees of freedom and the maximum spatial operation range. The label from the labeling mechanism cruising and fitting to the special-shaped surface target processed object is actually the embodiment of the spatial dimension mapping movement. Therefore, the spatial coordinate domain can provide a series of spatial dimension mapping basis for the labeling mechanism to perform the operation transformation trajectory cruise under the premise of ideal cruise posture control, effectively improving the label fitting alignment of the subsequent labeling mechanism according to the best fitting position cruise. Among them, the discrete space coordinate array of the labeling mechanism that reaches the ideal cruising posture to perform label fitting is the coordinate set before the labeling mechanism fits the label to the special-shaped surface in the form of an ideal cruising posture, that is, the first discretized space coordinate array; and the discrete space coordinate array when the target object is located in the specified labeling processing area is the coordinate set that the label needs to be aligned and fitted in the specified labeling processing area on the special-shaped surface, that is, the second discretized space coordinate array.

[0026] It should be noted that, as can be seen from the above, the accuracy of the label's alignment on the irregular surface can be achieved by accurately mapping the first discretized space coordinate array to the second discretized space coordinate array under the control of the ideal cruising posture. Therefore, this method cruise maps the first discretized space coordinate array to the second discretized space coordinate array based on the mapping clues preset by the operation transformation trajectory, but at this time there is a mapping distance difference between the coordinate pairs, which reflects the degree of spatial dependence required for label alignment and lamination, that is, the control amount required for alignment and lamination. Therefore, the Manhattan distance of each discrete space coordinate pair is continuously calculated one by one during the cruise mapping process to generate a cruise mapping distance matrix to clarify the deviation after coordinate mapping, so as to facilitate the label alignment optimization control of the labeling mechanism. Then, the labeling mechanism is analyzed and calculated for alignment control based on the semi-variance Gaussian model constructed by the cruise mapping distance matrix, so that the labeling mechanism can accurately align and fit the label to the irregular surface labeling area of ​​the target object based on the cruise of the optimal fitting position. This method can realize the PLC labeling machine's special-shaped surface labeling alignment control based on the fitting position cruise, making the labeling control for the special-shaped surface more stable, firm and accurate, ensuring the labeling quality of the processed object and reducing the labeling rework rate and cost output of the special-shaped surface.

[0027] More specifically, the method constructs a semi-variance Gaussian model of each discrete spatial coordinate pair between the first discretized spatial coordinate array and the second discretized spatial coordinate array according to the cruise mapping distance matrix, solves the weighted coefficient of the corresponding interpolation point of each discrete spatial coordinate pair through the spatial correlation index identified by the semi-variance Gaussian model, draws a cruise operation contour map based on the weighted coefficient interpolation, and controls the labeling mechanism according to the cruise operation contour map, which specifically includes the following steps: Determine the semivariance of each discrete spatial coordinate pair between the first discretized spatial coordinate array and the second discretized spatial coordinate array according to the cruise mapping distance matrix, and obtain a plurality of semivariance functions; The maximum likelihood method is introduced to perform maximization calculation of exponential likelihood fitting on the several semivariance functions to obtain a series of semivariance parameters, and a semivariance Gaussian model is constructed according to the series of semivariance parameters; wherein the semivariance model parameters include the range and effective distance of spatial association; Based on the semivariance function, identification is performed in the semivariance Gaussian model to determine the spatial correlation index of the first discretized space coordinate array reaching the second discretized space coordinate array according to the operation transformation trajectory under the premise of the ideal cruise posture; According to the spatial correlation index, the labeling mechanism is described in the premise that the labeling label is in an ideal cruise posture according to the operation transformation trajectory to carry the labeling label to fit the target processing object. The cruise mapping interpolation equations are transformed into an augmented matrix by elimination and back-substitution to solve, and the weight coefficient of each discrete space coordinate to the corresponding point to be interpolated is obtained; A contour interpolation map of the labeling mechanism control is constructed based on the given degrees of freedom, and a contour map of the labeling mechanism cruising under the given degrees of freedom constraints is generated by interpolating the weighted coefficients of each discrete spatial coordinate corresponding to the point to be interpolated in the contour interpolation map one by one, and marked as a cruising operation contour map; According to the cruise operation contour map, the cruise control decision of the labeling mechanism for the best fitting position on the special-shaped surface of the target processing object is planned to obtain a cruise control strategy. Based on the cruise control strategy, the labeling mechanism is controlled to fit the target processing object according to the operation transformation trajectory under the premise that the labeling mechanism is in an ideal cruise position, and a labeling control scheme is obtained.

[0028] It should be noted that for the spatial mapping dependence of each discrete spatial coordinate pair between the first discretized spatial coordinate array and the second discretized spatial coordinate array in the cruise mapping distance matrix, this method is embodied by the semi-variance Gaussian model fitted by the semi-variance function, reflecting the spatial autocorrelation of the labeling mechanism carrying the label from the first discretized spatial coordinate array to the second discretized spatial coordinate array under cruise control, revealing the spatial control vector structure of the labeling mechanism based on cruising at the fitting site, and providing an interpolation basis for the subsequent cruise mapping interpolation of the alignment labeling. The semi-variance Gaussian model is used to identify the spatial correlation index of the labeling mechanism carrying the label from the first discretized space coordinate array to the second discretized space coordinate array under the premise of the ideal cruise posture. The spatial correlation index reflects the control quantity framework required for the labeling mechanism to cruise and fit the label to the special-shaped surface, including the labeling control feed amount, the labeling control angle, and the labeling control scheduling. According to the spatial correlation index, the method visualizes the cruise control interpolation of the labeling mechanism on the control quantity framework by solving the cruise mapping interpolation equation group, and draws it as a cruise operation contour map, so as to improve the accuracy of various control parameters when the labeling mechanism cruises and carries the label on the special-shaped surface of the target workpiece based on the fitting site, ensure the integrity and control rationality of the PLC labeling machine for the special-shaped surface label fitting, improve the labeling efficiency, optimize the labeling quality, and reduce the phenomenon of wrinkles, bubbles or fitting dislocation during labeling.

[0029] More specifically, the step S108 includes the following steps: Obtain the label fitting log of the PLC labeling machine, extract the fitting defective rate of the labeling head for the labeling label through the label fitting log, and if the fitting defective rate is higher than the minimum fitting defective rate, obtain the specified fitting position of the labeling label; Obtain the model and specification information of the labeling head, extract the basic grasping size of the grasping joint corresponding to each specified fitting site on the labeling head through the model and specification information, and preset the fitting site area marginal threshold based on the basic grasping size; Constructing a fitting site field for labeling labels, dividing the fitting site field into M sub-fields based on the number of specified fitting sites, and performing fitting site inclusion planning for each sub-field until a boundary constraint of a fitting site region marginal threshold is reached, thereby obtaining each sub-fitting site distribution field; Extract the stickiness index of each sub-sticking site distribution area of ​​the labeled label under the defective sticking rate through the label sticking log; if the stickiness index exceeds the unqualified stickiness index, extract the sub-sticking site distribution area corresponding to the stickiness index and mark it as an unreasonable sub-sticking site distribution area; Obtaining an expected fitting requirement of the labeling label, obtaining an expected adhesion index of the labeling label according to the expected fitting requirement, calculating a deviation of the adhesion index of the unreasonable sub-fitting site distribution area compared to the expected adhesion index, and obtaining an adhesion index deviation value; According to the distribution field of unreasonable sub-fitting sites, a solution space of fitting sites is constructed, and several fitting site solutions remaining in the solution space excluding the specified fitting sites are obtained. Each fitting site solution is evaluated based on the stickiness index deviation value to obtain a solution evaluation value, and only the fitting site solution corresponding to the maximum solution evaluation value is extracted and marked as the best fitting site solution; The optimal fitting site matrix is ​​constructed by obtaining the optimal fitting site solution of each unreasonable sub-fitting site distribution field. The grabbing site and fitting force of the labeling head for the labeling label are controlled according to the optimal fitting site matrix to improve the poor adhesion phenomenon.

[0030] It should be noted that most of the existing PLC labeling machines use a labeling head composed of multiple grasping joints to grasp the labeling label. Multiple grasping joints can greatly improve the grasping movement and bonding stability of the labeling label. However, if the grasping joints do not properly control the grasping position and bonding strength of the labeling label, it may cause the labeling label to be loosely adhered or loosely adhered to the target workpiece, causing the label to fall off during transportation, greatly affecting the quality of label bonding. In this regard, this method can optimize the bonding site based on the deviation required for the adhesion index of each local area of ​​the labeling label to reach the expected adhesion index under the defective bonding rate, so that the labeling head can control the grasping position and bonding strength of the labeling label more reasonably and accurately, which can improve the bonding and adhesion firmness of the label and reduce the defective bonding rate and scrap rate of the PLC labeling machine.

[0031] In addition, the PLC labeling machine control method based on fitting site cruising also includes the following steps: The label laminating log of the PLC labeling machine is used to extract the laminating rework assessment rate of the laminating head for the labeling label. If the laminating wrinkle rework rate accounts for a proportion in the laminating rework assessment rate that exceeds the preset proportion, then some samples are extracted from the target processed object corresponding to the laminating wrinkle rework rate, which are defined as abnormal laminating samples. The label-bonding area on the abnormally bonded sample is photographed by an industrial visual camera mounted on the PLC labeling machine to obtain image data of the label-bonding area, and the gray-level co-occurrence matrix algorithm is introduced to perform wrinkle recognition on the image data to obtain the actual bonding wrinkle features of the label-bonding area; Based on the actual laminating wrinkle features, an actual laminating wrinkle model diagram of the label laminating area is constructed, and a preset control strategy of the PLC labeling machine is obtained through the label laminating log, and based on the preset control strategy, the actual laminating tension applied by each grabbing joint of the labeling head when the labels in the label laminating area are jointly grabbed is extracted; The mechanical mechanics knowledge graph is obtained through the big data network, and the mechanical control principle and activity distribution pattern of each grabbing joint on the labeling head are obtained at the same time. Based on the mechanical control principle, activity distribution pattern and basic grabbing size, the actual fitting tension is identified in the mechanical mechanics knowledge graph, and the force weight of each grabbing joint when the labeling head jointly grabs to generate the actual fitting tension is obtained; A K-means clustering algorithm is introduced, and clustering calculation is performed on the actual fitting wrinkle model graph in association with each grabbing joint based on the force weight in the K-means clustering algorithm to obtain a fitting tension association clustering result; Through the clustering results of the bonding tension association, one or more gripping joints on the labeling head that cause actual bonding wrinkles in the label bonding are determined and marked as abnormal force gripping joints. The force of one or more abnormal force gripping joints is adjusted and controlled to eliminate the bonding wrinkles of the label by the bonding head.

[0032] It should be noted that the labeling head of a PLC labeling machine usually moves and cruises to the label fitting area of ​​the target workpiece through the joint grasping control of multiple grasping joints on the labeling head. However, some existing PLC labeling machines have large errors in the joint control of multiple grasping joints on the labeling head, which causes improper fitting tension to be applied when the multiple grasping joints grasp the labels. Too tight or too loose fitting tension will cause the label to have fitting wrinkles in the label fitting area. The fitting wrinkles will cause the display of the content on the label to be distorted, which is not conducive to reading and identification during use, and reduces the reading and identification performance and quality of the label itself. Therefore, this method can be used to perform correlation tracing calculation on whether each grasping joint causes actual fitting wrinkle characteristics according to the actual fitting tension applied by the labeling head when jointly grasping the labels in the label fitting area. According to the final clustering result, the grasping joints corresponding to the actual fitting wrinkle characteristics can be quickly obtained, so as to further adjust the grasping force, so as to eliminate the fitting wrinkle phenomenon caused by improper joint grasping control of all grasping joints, improve the label fitting quality of the labeling head, avoid excessive fitting wrinkle defects, and reduce the labeling rework rate and labeling cost output.

[0033] The second aspect of the present invention provides a PLC labeling machine control system based on fitting site cruising, such as Figure 3As shown, the PLC labeling machine control system includes a memory 31 and a processor 32. The memory 31 stores a PLC labeling machine control method program based on fitting site cruising. When the PLC labeling machine control method program is executed by the processor 32, any one of the steps of the PLC labeling machine control method is implemented.

[0034] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A PLC labeling machine control method based on fitting site cruising, characterized in that: The following steps are involved: S102: Acquire continuous image data of the labeling conveyance of the target object, define the best fitting position array of the target object according to the specification parameter information of the labeling label and the curvature of the labeling area surface of the target object, and linearly track the optical flow motion of the continuous image data based on the sliding window neighborhood preset in the best fitting position array to obtain the operation transformation trajectory of the best fitting position on the target object within a preset time period; S104: Acquire the tracking posture change of the industrial vision camera through the industrial vision camera position tracking the operation transformation trajectory, and acquire inertial measurement data, describe the posture time series state variables of the tracking posture change based on the pre-integration relativity of the inertial measurement data to the continuous image data, and obtain the ideal cruising posture of the labeling mechanism cruising and fitting relative to the best fitting position on the target workpiece under the operation transformation trajectory condition; S106: If the cruise function mode equipped with the PLC labeling machine is not compatible with the automatic cruise planning of the operation transformation trajectory corresponding to the profiled surface based on the ideal cruise posture as the control premise, then based on the cruise mapping distance matrix for labeling to the specified labeling processing area in the operation transformation trajectory mapping space coordinate domain, the labeling mechanism is controlled according to the semi-variance Gaussian model identification and solution of the cruise mapping distance matrix; S108: If the defective fitting rate of the labeling head for the labeling label is higher than the minimum defective fitting rate, the unreasonable sub-fitting site distribution field of the labeling label is extracted according to the criterion that the stickiness index exceeds the unqualified stickiness index, and the unreasonable sub-fitting site distribution field is screened for the best fitting site solution according to the expected fitting requirements of the labeling label to obtain the best fitting site matrix, and the fitting of the labeling head is controlled according to the best fitting site matrix.

2. According to claim 1, a PLC labeling machine control method based on fitting site cruising is characterized in that: The step S102 specifically includes the following steps: Obtaining a specified labeling processing area of ​​the processing and transportation mechanism, and photographing the target processing object that begins to enter the specified labeling processing area within a preset time period frame by frame through an industrial vision camera mounted on the labeling mechanism, so as to obtain continuous image data of the target processing object during the labeling processing operation; Obtaining specification parameter information of the labeling label, synchronously obtaining the labeling task of the target object and the surface curvature of the labeling area, and planning and defining the fitting site of the surface curvature of the labeling area based on the specification parameter information and the labeling task to obtain the optimal fitting site array of the target object; A local sliding window neighborhood of pixels of the fitting sites is preset based on the matrix pattern expressed by the best fitting site array, and a Sobel operator is introduced to calculate the spatial gradient and the temporal gradient of every two adjacent frames of the continuous image data; The local sliding window area is divided into N sub-neighborhoods based on the number of best fitting sites, and for each sub-neighborhood, the linear dynamic motion of each sub-neighborhood is described by the optical flow components of the best fitting site pixels in the horizontal direction and the vertical direction, and the spatial gradient and the temporal gradient tracking, to generate an optical flow equation for each sub-neighborhood; Solve the optical flow equation to obtain the optical flow solution of each best fitting point pixel in each sub-neighborhood, repeat the above steps of calculating the spatial gradient and the temporal gradient and tracking and solving the optical flow equation in the image frame until each frame of the continuous image data is calculated and the iteration is stopped; After the iteration stops, the optical flow solutions of all the best fitting points in the continuous image data are output, and the operation transformation trajectory of the best fitting point on the target processing object within a preset time period is determined based on the optical flow solutions of all the best fitting points.

3. A PLC labeling machine control method based on fitting site cruising according to claim 1, characterized in that: The step S104 specifically includes the following steps: Tracking the operation transformation trajectory of the best fitting point on the target workpiece within the preset time period by an industrial vision camera to obtain the tracking posture change of the industrial vision camera cruising the best fitting point on the surface curvature of the labeling area; During the site tracking process, the inertial measurement unit carried on the labeling mechanism is used to collect inertial measurement data when the industrial visual camera runs the tracking posture change; wherein the inertial measurement data includes the angular velocity, linear acceleration and nonlinear acceleration of the gyroscope; Taking tracking posture changes as a constraint premise, the inertial measurement data is used to perform pre-integration calculations on the acceleration and angular velocity of the continuous image data during the labeling process of the target object to obtain a pre-integration result of the relative posture change, and the Kalman filter is used to perform a time-series state fusion of the tracking posture change and the pre-integration result to obtain a posture time-series state variable matrix; Extract the pre-integration error value of the inertial measurement data through the pre-integration result, depict the pose nodes based on the pose timing state variable matrix, define the state variable error constraints between each pose node based on the pre-integration error value, and construct the pose timing state model diagram according to the pose nodes and the state variable error constraints between each pose node; Taking the operation transformation trajectory as the target rule, presetting the square residual function according to the state variable error constraint, calculating the gradient of the square residual function with respect to the target rule, and generating the current approximate Hessian matrix of the posture timing state model diagram and the current gradient search direction according to the gradient; Preset the iteration depth threshold, perform linear iterative update on the state of each pose node in the current Hessian matrix through the current gradient search direction until the square residual function converges to the iteration depth threshold, and output the updated approximate Hessian matrix; The ideal cruising position of the labeling mechanism for cruising and fitting the best fitting position on the target object under the condition of the operation transformation trajectory is determined based on the state variable estimation of the updated approximate Hessian matrix.

4. A PLC labeling machine control method based on fitting site cruising according to claim 1, characterized in that: The step S106 specifically includes the following steps: Acquire functional mode information of the PLC labeling machine, and determine whether the autonomous cruise function equipped by the PLC labeling machine is compatible with the automated cruise planning of the corresponding operation change trajectory of the special-shaped surface based on the ideal cruise posture as the control premise according to the functional mode information; If the cruise function mode equipped with the PLC labeling machine is not compatible with the automatic cruise planning of the corresponding operation change trajectory of the special-shaped surface based on the ideal cruise posture as the control premise, then the initial design drawing of the labeling mechanism is obtained; Based on the initial design draft, the predetermined degrees of freedom and the maximum spatial operating range of the labeling mechanism are obtained, the spatial coordinates of the predetermined degrees of freedom are constructed by assigning the maximum spatial operating range as a boundary frame, and a spatial coordinate domain for the labeling mechanism to achieve the predetermined degrees of freedom is generated; Obtaining a discrete spatial coordinate array of the labeling mechanism to achieve an ideal cruising posture for labeling through a spatial coordinate domain, which is defined as a first discretized spatial coordinate array, and obtaining a discrete spatial coordinate array when the target object is located in a specified labeling processing area, which is defined as a second discretized spatial coordinate array; Based on the preset mapping clues of the operation transformation trajectory, the first discretized space coordinate array is cruise mapped to the second discretized space coordinate array according to the mapping clues as the target reference, and the Manhattan distance of each discrete space coordinate pair is continuously calculated one by one during the cruise mapping process to obtain a cruise mapping distance matrix; According to the cruise mapping distance matrix, a semi-variance Gaussian model of each discrete spatial coordinate pair between the first discretized spatial coordinate array and the second discretized spatial coordinate array is constructed, and the weighted coefficient of the corresponding interpolation point of each discrete spatial coordinate pair is solved by the spatial correlation index identified by the semi-variance Gaussian model. The cruise operation contour map is interpolated based on the weighted coefficient, and the labeling mechanism is controlled according to the cruise operation contour map.

5. A PLC labeling machine control method based on fitting site cruising according to claim 4, characterized in that: The method of constructing a semi-variance Gaussian model of each discrete spatial coordinate pair between the first discretized spatial coordinate array and the second discretized spatial coordinate array according to the cruise mapping distance matrix, solving the weighted coefficient of the corresponding interpolation point of each discrete spatial coordinate pair through the spatial correlation index identified by the semi-variance Gaussian model, drawing a cruise operation contour map based on the weighted coefficient interpolation, and controlling the labeling mechanism according to the cruise operation contour map specifically includes the following steps: Determine the semivariance of each discrete spatial coordinate pair between the first discretized spatial coordinate array and the second discretized spatial coordinate array according to the cruise mapping distance matrix, and obtain a plurality of semivariance functions; The maximum likelihood method is introduced to perform maximization calculation of exponential likelihood fitting on the several semivariance functions to obtain a series of semivariance parameters, and a semivariance Gaussian model is constructed according to the series of semivariance parameters; wherein the semivariance model parameters include the range and effective distance of spatial association; Based on the semivariance function, identification is performed in the semivariance Gaussian model to determine the spatial correlation index of the first discretized space coordinate array reaching the second discretized space coordinate array according to the operation transformation trajectory under the premise of the ideal cruise posture; According to the spatial correlation index, the labeling mechanism is described in the premise that the labeling label is in an ideal cruise posture according to the operation transformation trajectory to carry the labeling label to fit the target processing object. The cruise mapping interpolation equations are transformed into an augmented matrix by elimination and back-substitution to solve, and the weight coefficient of each discrete space coordinate to the corresponding point to be interpolated is obtained; A contour interpolation map of the labeling mechanism control is constructed based on the given degrees of freedom, and a contour map of the labeling mechanism cruising under the given degrees of freedom constraints is generated by interpolating the weighted coefficients of each discrete spatial coordinate corresponding to the point to be interpolated in the contour interpolation map one by one, and marked as a cruising operation contour map; According to the cruise operation contour map, the cruise control decision of the labeling mechanism for the best fitting position on the special-shaped surface of the target processing object is planned to obtain a cruise control strategy. Based on the cruise control strategy, the labeling mechanism is controlled to fit the target processing object according to the operation transformation trajectory under the premise that the labeling mechanism is in an ideal cruise position, and a labeling control scheme is obtained.

6. A PLC labeling machine control method based on fitting site cruising according to claim 1, characterized in that: The step S108 specifically includes the following steps: Obtain the label fitting log of the PLC labeling machine, extract the fitting defective rate of the labeling head for the labeling label through the label fitting log, and if the fitting defective rate is higher than the minimum fitting defective rate, obtain the specified fitting position of the labeling label; Obtain the model and specification information of the labeling head, extract the basic grasping size of the grasping joint corresponding to each specified fitting site on the labeling head through the model and specification information, and preset the fitting site area marginal threshold based on the basic grasping size; Constructing a fitting site field for labeling labels, dividing the fitting site field into M sub-fields based on the number of specified fitting sites, and performing fitting site inclusion planning for each sub-field until a boundary constraint of a fitting site region marginal threshold is reached, thereby obtaining each sub-fitting site distribution field; Extract the stickiness index of each sub-sticking site distribution area of ​​the labeled label under the defective sticking rate through the label sticking log; if the stickiness index exceeds the unqualified stickiness index, extract the sub-sticking site distribution area corresponding to the stickiness index and mark it as an unreasonable sub-sticking site distribution area; Obtaining an expected fitting requirement of the labeling label, obtaining an expected adhesion index of the labeling label according to the expected fitting requirement, calculating a deviation of the adhesion index of the unreasonable sub-fitting site distribution area compared to the expected adhesion index, and obtaining an adhesion index deviation value; According to the distribution field of unreasonable sub-fitting sites, a solution space of fitting sites is constructed, and several fitting site solutions remaining in the solution space excluding the specified fitting sites are obtained. Each fitting site solution is evaluated based on the stickiness index deviation value to obtain a solution evaluation value, and only the fitting site solution corresponding to the maximum solution evaluation value is extracted and marked as the best fitting site solution; The optimal fitting site matrix is ​​constructed by obtaining the optimal fitting site solution of each unreasonable sub-fitting site distribution field. The grabbing site and fitting force of the labeling head for the labeling label are controlled according to the optimal fitting site matrix to improve the poor adhesion phenomenon.

7. A PLC labeling machine control system based on fitting site cruising, characterized in that: The PLC labeling machine control system includes a memory and a processor. The memory stores a PLC labeling machine control method program based on fitting site cruising. When the PLC labeling machine control method program is executed by the processor, the PLC labeling machine control method steps as described in any one of claims 1 to 6 are implemented.

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