Control method, system, device and storage medium of automatic laminating equipment

Abstract

The present invention relates to the technical field of automatic control equipment, and discloses a control method, system, device and storage medium for automatic laminating equipment, wherein the method comprises recording the movement data of multiple laminating components in the laminating equipment by image acquisition and position sensing according to the laminating operation of the laminating equipment, and obtaining movement records; calculating the X-axis offset degree and Y-axis offset degree of each laminating component; optimizing the model parameters by the least square method and genetic algorithm, constructing a laminating model, and formulating a laminating plan according to the laminating model; and controlling the movement of the laminating component according to the laminating plan to complete the laminating operation of the laminating component and the component to be laminating. The automatic laminating control method provided by the present invention effectively improves the laminating quality and efficiency, and solves the problem of unstable laminating quality in the traditional laminating process.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control equipment, and in particular to a control method, system, device and storage medium for automatic laminating equipment. Background Art

[0002] At present, automated laminating equipment plays a vital role in modern manufacturing. They use automation technology to achieve precise laminating of items, significantly reducing labor costs and improving production efficiency. This type of equipment is widely used in different production fields. For example, in the packaging industry, automated binding machines are a common laminating equipment. This is especially true for automated laminating machines, which not only have important applications in the packaging field, but are also indispensable in many industries such as machinery manufacturing. With the continuous deepening of industrial automation, the requirements for the accuracy and reliability of automated laminating equipment are also getting higher and higher. In order to meet these growing demands, the design and manufacture of automated laminating equipment must be continuously optimized to ensure that they can provide stable and accurate laminating effects in various production environments.

[0003] In one prior art, an automated bonding device includes a machine, a pair of carriers, a suction device, and an elastic bonding device connected to the machine. The suction device is used to suck one piece to be bonded, and the elastic bonding device is used to suck another piece to be bonded, and elastically bond the other piece to be bonded to the piece to be bonded toward the suction device. However, after the product to be bonded is replaced, the automated bonding device needs to adjust the suction nozzle to match the product size. The overall structure of the existing solution is too complicated. When faced with adjustment control, it is difficult to timely provide the optimal bonding solution when facing bonding pieces of different sizes.

[0004] In the prior art, due to the low matching efficiency of the bonding solution, the bonding process may have problems such as inaccurate alignment, uneven bonding or improper bonding force, which in turn leads to low bonding quality between the bonding component and the component to be bonded. Summary of the invention

[0005] The present invention provides a control method, system, device and storage medium of automatic laminating equipment to solve the problem of low laminating quality between a laminating component and a component to be laminarized.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a control method for automatic lamination, comprising:

[0007] According to the laminating operation of the laminating device, the movement data of multiple laminating components in the laminating device are recorded by image acquisition and position sensing to obtain a movement record; wherein the laminating components include rollers, clamps, brackets connected to the laminating device and a visual recognition system, and the movement record includes historical laminating data of each laminating component;

[0008] Obtaining a standard moving speed and a standard moving position of the bonding component, and calculating an X-axis offset degree and a Y-axis offset degree of each of the bonding components according to the standard moving speed and the standard moving position;

[0009] Acquire the dimension data of the bonding component, and perform fitting operation using the least square method according to the preset first model parameters, the preset second model parameters, the dimension data of the bonding component and the historical bonding data to obtain the fitted first model parameters and the fitted second model parameters; wherein the dimension data of the bonding component includes the type and size of the bonding component; the preset first model parameters include the X-axis offset degree and the Y-axis offset degree, and the preset second model parameters include the force applied by the bonding component and the distance between the bonding component and the component to be bonded, and the component to be bonded includes a display screen, a touch screen, a double-sided tape and a decorative panel;

[0010] According to the fitted first model parameters and the fitted second model parameters, a genetic algorithm is used to perform optimization to obtain an optimal first model parameter and an optimal second model parameter;

[0011] Constructing a fitting model according to the optimal first model parameter and the optimal second model parameter;

[0012] Acquire the size data of the component to be bonded, and input the bonding model to perform a fitting operation according to the size data of the component to be bonded, the optimal first model parameter, and the optimal second model parameter, so as to obtain a bonding solution; wherein the size data of the component to be bonded includes the type, size, edge protrusion distance, and standard moving distance of the component to be bonded, as well as the overall width and overall height of the component to be bonded;

[0013] Calculate the edge protrusion value and the offset value according to the edge protrusion distance of the component to be bonded, the overall width of the component to be bonded, the standard moving speed of the component to be bonded, and the actual moving speed of the component to be bonded;

[0014] Compare the edge protrusion value and the offset value with a preset edge protrusion value threshold and a preset offset value threshold, respectively, to obtain a comparison result; if the comparison result is normal, then the operation is performed normally; if the comparison result is abnormal, then output a no-fitting solution;

[0015] According to the bonding scheme, the movement of the bonding component is controlled to complete the bonding operation of the bonding component and the component to be bonded.

[0016] In an optional implementation, the calculating, according to the standard moving speed and the standard moving position, the X-axis offset degree and the Y-axis offset degree of each of the bonding components comprises:

[0017] Acquiring the moving speed of the laminating component;

[0018] Performing a subtraction operation according to the standard moving speed and the moving speed of the bonding component to obtain a speed difference;

[0019] Calculate the proportion of the speed difference in the standard moving speed to obtain the X-axis offset degree of the bonding component;

[0020] Acquiring the moving position of the laminating component;

[0021] Performing a subtraction operation based on the standard moving position and the moving position of the fitting component to obtain a position difference;

[0022] The proportion of the position difference in the standard moving position is calculated to obtain the Y-axis offset degree of the fitting component.

[0023] In an optional implementation, the fitting operation is performed using the least square method according to the preset first model parameters, the preset second model parameters, the size data of the bonding component and the historical bonding data to obtain the fitted first model parameters and the fitted second model parameters, including:

[0024] A multivariate linear regression model is defined according to the preset first model parameters, the preset second model parameters and the size data of the fitting component, and the formula is as follows:

[0025] Y=a 0 +(a x +a y +β)·s+∈;

[0026] Among them, a 0 is a constant, a x and a y is the X-axis offset degree and the Y-axis offset degree in the preset first model parameters, β is the pressure coefficient obtained by fitting the second model parameters, s is the sum of the overall width, overall height and maximum thickness in the dimensional data of the fitting component, and ∈ is an error term;

[0027] Perform a fitting operation using the least square method to obtain a first fitting model parameter and a second fitting model parameter;

[0028] The least square method is implemented by minimizing the sum of squares of the errors between the predicted value and the actual value. The formula is as follows:

[0029]

[0030] Wherein, Yi is the actual bonding gap size in the historical bonding data, is the predicted fit gap size, Calculated from the multiple linear regression model, m is the total number of observations.

[0031] In an optional implementation, the using a genetic algorithm to perform optimization according to the fitted first model parameter and the fitted second model parameter to obtain an optimal first model parameter and an optimal second model parameter includes:

[0032] According to the fitted first model parameters and the fitted second model parameters, random disturbances are introduced to generate multiple groups of parameters as initial populations of the genetic algorithm;

[0033] The fitness of each individual in the initial population is calculated to obtain the first generation fitness. The fitness function is defined as the sum of squares of the errors between the historical fitting data and the fitting model prediction. The specific formula of the fitness function is as follows:

[0034]

[0035] Among them, j∈{1, 2, ..., m}, m is the total number of generated individuals, F j is the fitness of the jth individual, y i is the fitting position in the historical fitting data, is the fitting position predicted by the fitting model, F i is the fitting force in the historical fitting data, The fitting force predicted by the fitting model;

[0036] According to the first-generation fitness, a preset number of individuals are selected from a plurality of individuals in descending order of fitness to be randomly paired and exchange genes, the value of a certain parameter is changed with a preset probability, and the fitness of the individual with the highest fitness is set as the first fitness;

[0037] Iterate according to the fitness function to obtain the fitness of the new generation of individuals, and record the number of iterations;

[0038] Determine whether the fitness of the new generation of individuals is better than the first fitness, if so, update the fitness of the new generation of individuals to the first fitness; if not, discard the fitness of the first generation of individuals;

[0039] Continue to the next iteration, when the number of iterations is greater than or equal to the preset number of iterations, output the current first fitness as the optimal fitness;

[0040] The individual parameters corresponding to the optimal fitness include the optimal first model parameters and the optimal second model parameters.

[0041] In an optional implementation, constructing a fitting model according to the optimal first model parameter and the optimal second model parameter includes:

[0042] According to the optimal first model parameters, a fitting trajectory model is constructed, and the formula of the fitting trajectory model is as follows:

[0043]

[0044] in, is the position vector of the bonding component at time t, including the positions of the X-axis and the Y-axis, is the initial velocity vector of the fitted component, is the acceleration vector of the fitted component, α X and α Y are the X-axis offset degree and the Y-axis offset degree, respectively, and are the X-axis and Y-axis coordinates of the initial position of the bonding component, and are the X-axis and Y-axis coordinates of the origin of the moving coordinate system, and t is the moving time;

[0045] According to the optimal second model parameters, a fitting force model is constructed, and the formula of the fitting force model is as follows:

[0046]

[0047] Wherein, β is the pressure coefficient, f is the force applied by the bonding component, and d is the distance between the bonding component and the component to be bonded;

[0048] Wherein, the fitting model includes the fitting trajectory model and the fitting force model.

[0049] In an optional embodiment, the bonding solution includes:

[0050] The bonding plan includes movement parameters, movement time, movement distance, movement time and bonding force of the bonding component to be bonded to the component to be bonded;

[0051] When the fitting component is a roller, the motion parameters include the rotation angle, rotation speed and radius of the roller;

[0052] When the fitting component is a clamping plate, the motion parameters include a rotation angle, a rotation acceleration and a radius.

[0053] In an optional embodiment, the edge protrusion value and the offset degree value are calculated based on the edge protrusion distance of the component to be bonded, the overall width of the component to be bonded, the standard moving speed of the bonding component, and the actual moving speed of the bonding component, including:

[0054] The ratio of the edge protrusion distance of the component to be bonded to the overall width of the component to be bonded is calculated to obtain the edge protrusion value. The calculation formula of the edge protrusion value is as follows:

[0055]

[0056] Wherein, EF is the edge protrusion value, E is the edge protrusion distance of the component to be bonded, and W is the overall width of the component to be bonded;

[0057] Subtracting the standard moving speed of the laminating component from the actual moving speed of the laminating component to obtain a moving speed difference;

[0058] The proportion of the moving speed difference to the standard moving speed of the bonding component is calculated to obtain the offset value. The calculation formula of the offset value is as follows:

[0059]

[0060] Wherein, SD is the deviation degree value, Vs is the standard moving speed, and Va is the actual moving speed.

[0061] In an optional embodiment, controlling the movement of the bonding component according to the bonding scheme to complete the bonding operation of the bonding component and the component to be bonded includes:

[0062] Moving the fitting component to an initial position;

[0063] According to the moving speed and moving position parameters of the bonding component required in the bonding scheme, the bonding component is controlled to perform a moving operation, and the bonding component and the component to be bonded are bonded according to the bonding force requirements of the bonding scheme to obtain a first bonding result;

[0064] According to the first bonding result, detecting whether there is a defect in the bonding area by an image algorithm to obtain a defect detection result; if the defect detection result is abnormal, adjusting the bonding plan to obtain a second bonding plan; if the defect detection result is no abnormality, the bonding plan is the second bonding plan;

[0065] According to the second bonding scheme, the bonding component is controlled to move to obtain a second bonding result, thereby completing the bonding operation of the bonding component and the component to be bonded.

[0066] In a second aspect, the present invention provides a control system for an automated laminating device, comprising:

[0067] A movement record acquisition module, used to record movement data of a plurality of laminating components in the laminating device by means of image acquisition and position sensing according to the laminating operation of the laminating device, so as to obtain movement records; wherein the laminating components include rollers, clamps, brackets connected to the laminating device and a visual recognition system, and the movement records include historical laminating data of each laminating component;

[0068] A standard data acquisition module, used to acquire a standard moving speed and a standard moving position of the bonding component;

[0069] A deviation degree calculation module, used for calculating the X-axis deviation degree and the Y-axis deviation degree of each of the bonding components according to the standard moving speed and the standard moving position;

[0070] A fitting component size data acquisition module, used to acquire the size data of the fitting component; wherein the size data of the fitting component includes the type and size of the fitting component;

[0071] A fitting model parameter acquisition module, used to perform a fitting operation using the least square method according to a preset first model parameter, a preset second model parameter, the size data of the bonding component and the historical bonding data, to obtain a fitting first model parameter and a fitting second model parameter; wherein the preset first model parameter includes the X-axis offset degree and the Y-axis offset degree, and the preset second model parameter includes the force applied by the bonding component and the distance between the bonding component and a component to be bonded, and the component to be bonded includes a display screen, a touch screen, a double-sided adhesive tape and a decorative panel;

[0072] An optimal model parameter acquisition module, used to use a genetic algorithm to perform optimization according to the fitted first model parameter and the fitted second model parameter to obtain an optimal first model parameter and an optimal second model parameter;

[0073] A fitting model construction module, used to construct a fitting model according to the optimal first model parameter and the optimal second model parameter;

[0074] A module for acquiring the size data of the parts to be bonded, used for acquiring the size data of the parts to be bonded; wherein the size data of the parts to be bonded include the type, size, edge protrusion distance and standard moving distance of the parts to be bonded, and the overall width and overall height of the parts to be bonded;

[0075] A fitting scheme acquisition module is configured to input the fitting model to perform fitting operation according to the size data of the component to be fitted, the first fitting model parameter and the second fitting model parameter, so as to obtain a fitting scheme; calculate an edge protrusion value and an offset value according to the edge protrusion distance of the component to be fitted, the overall width of the component to be fitted, the standard moving speed of the fitting component and the actual moving speed of the fitting component; compare the edge protrusion value and the offset value with a preset edge protrusion value threshold and a preset offset value threshold, respectively, to obtain a comparison result; if the comparison result is normal, the module operates normally; if the comparison result is abnormal, the module outputs that no fitting scheme is available;

[0076] The laminating operation module is used to control the movement of the laminating component according to the laminating scheme, so as to complete the laminating operation of the laminating component and the component to be laminarized.

[0077] In a third aspect, the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the control method of the automated bonding equipment described in any one of the above is implemented.

[0078] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the control methods of the automated bonding equipment described above.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] The present invention provides a control method for an automated laminating device, comprising: recording movement data of a plurality of laminating components in the laminating device by means of image acquisition and position sensing according to the laminating operation of the laminating device, and obtaining movement records; wherein the laminating components include rollers, clamps, brackets connected to the laminating device, and a visual recognition system, and the movement records include historical laminating data of each laminating component; obtaining a standard movement speed and a standard movement position of the laminating component, and calculating an X-axis deviation degree and a Y-axis deviation degree of each laminating component according to the standard movement speed and the standard movement position; obtaining the laminating operation; and obtaining a movement record of the laminating component. The dimension data of the bonding component is fitted by using the least square method according to the preset first model parameters, the preset second model parameters, the dimension data of the bonding component and the historical bonding data, so as to obtain the fitted first model parameters and the fitted second model parameters; wherein the dimension data of the bonding component includes the type and size of the bonding component; the preset first model parameters include the X-axis offset degree and the Y-axis offset degree, and the preset second model parameters include the force applied by the bonding component and the distance between the bonding component and the component to be bonded, and the component to be bonded includes a display screen, a touch screen, a double-sided tape and a decorative panel; According to the fitting first model parameters and the fitting second model parameters, a genetic algorithm is used to search for the best first model parameters and the best second model parameters; according to the best first model parameters and the best second model parameters, a fitting model is constructed; the size data of the parts to be bonded are obtained, and according to the size data of the parts to be bonded, the best first model parameters and the best second model parameters, the fitting model is input to perform a fitting operation to obtain a bonding solution; wherein the size data of the parts to be bonded includes the type, size, edge protrusion distance and standard movement distance of the parts to be bonded, and the size data of the parts to be bonded includes the type, size, edge protrusion distance and standard movement distance of the parts to be bonded, and the size data of the parts to be bonded includes the type, size, edge protrusion distance and standard movement distance of the parts to be bonded the overall width and overall height of the component to be bonded; according to the edge protrusion distance of the component to be bonded, the overall width of the component to be bonded, the standard moving speed of the bonding component and the actual moving speed of the bonding component, the edge protrusion value and the offset degree value are calculated; the edge protrusion value and the offset degree value are compared with the preset edge protrusion value threshold and the preset offset degree value threshold respectively to obtain a comparison result; if the comparison result is normal, the normal operation is performed, and if the comparison result is abnormal, no bonding plan is output; according to the bonding plan, the movement of the bonding component is controlled to complete the bonding operation of the bonding component and the component to be bonded.

[0081] The present invention provides a control method for an automated laminating device. First, a mobile record acquisition module is used to collect in real time the movement data of multiple laminating components in the laminating device, including the historical laminating data of a roller, a clamping plate, a bracket connected to the laminating device, and a visual recognition system. Then, the X-axis offset degree and the Y-axis offset degree of each laminating component are calculated by an offset degree calculation module. Then, these data and the preset first model parameters and second model parameters are used to perform a fitting operation by the least squares method to obtain the fitted first model parameters and the fitted second model parameters. In addition, the present invention also uses a genetic algorithm to optimize the model parameters obtained by fitting to obtain the optimal first model parameters and the optimal second model parameters. According to these optimal model parameters, a laminating model is constructed, and the size data of the components to be laminating are obtained, including the type, size, edge protrusion value, and standard moving distance. Then, according to the size data of the components to be laminating, the optimal first model parameters, and the optimal second model parameters, the laminating model is input for fitting operation to obtain a laminating solution. The present invention further obtains the edge protrusion distance and the overall width of the component to be bonded, calculates the edge protrusion value and the offset value, and compares them with the preset threshold value to determine whether it is operating normally or outputting no bonding solution. Finally, according to the bonding solution, the movement of the bonding component is controlled to complete the bonding operation of the bonding component and the component to be bonded. Through this series of operations, the present invention provides an intelligent and automated bonding equipment control solution, which greatly improves the bonding accuracy and efficiency, ensures product quality, and reduces uncertainty and waste in production.

[0082] The present invention provides a control method, system, device and storage medium for automated laminating equipment to solve the problem of low laminating quality between a laminating component and a component to be laminarized, and to achieve automated laminating with high efficiency and high precision. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a flow chart of a control method of an automated bonding device provided by an embodiment of the present invention;

[0084] Figure 2 It is a structural schematic diagram of a control system of an automated laminating device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0085] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0086] Reference Figure 1 The first embodiment of the present invention provides a control method for an automated laminating device, comprising the following steps:

[0087] S11, according to the bonding operation of the bonding device, recording the movement data of multiple bonding components in the bonding device by means of image acquisition and position sensing to obtain a movement record;

[0088] S12, obtaining a standard moving speed and a standard moving position of the bonding component, and calculating an X-axis offset degree and a Y-axis offset degree of each bonding component according to the standard moving speed and the standard moving position;

[0089] S13, obtaining the size data of the bonding component, and performing a fitting operation using a least square method according to the preset first model parameters, the preset second model parameters, the size data of the bonding component and the historical bonding data to obtain a fitting first model parameter and a fitting second model parameter;

[0090] S14, using a genetic algorithm to search for an optimal first model parameter and an optimal second model parameter according to the fitted first model parameter and the fitted second model parameter;

[0091] S15, constructing a fitting model according to the optimal first model parameter and the optimal second model parameter;

[0092] S16, obtaining the size data of the component to be bonded, and inputting the bonding model into the bonding model for fitting operation according to the size data of the component to be bonded, the optimal first model parameter and the optimal second model parameter, to obtain a bonding solution;

[0093] S17, calculating an edge protrusion value and a deviation degree value according to the edge protrusion distance of the component to be bonded, the overall width of the component to be bonded, the standard moving speed of the component to be bonded, and the actual moving speed of the component to be bonded;

[0094] S18, comparing the edge protrusion value and the offset value with a preset edge protrusion value threshold and a preset offset value threshold, respectively, to obtain a comparison result; if the comparison result is normal, the operation is performed normally; if the comparison result is abnormal, outputting a no-fitting solution;

[0095] S19, according to the bonding plan, controlling the movement of the bonding component to complete the bonding operation of the bonding component and the component to be bonded.

[0096] In step S11, according to the bonding operation of the bonding device, the movement data of multiple bonding components in the bonding device are recorded by image acquisition and position sensing to obtain a movement record.

[0097] The image acquisition method uses a high-resolution camera to record the movement of the bonding component in detail; the position sensing method uses a laser distance measurement method to track the real-time position of the bonding component; these two methods work together to provide a comprehensive movement record for the bonding component. The bonding component includes a roller, a clamp, a bracket connected to the bonding equipment, and a visual recognition system. The movement record includes the historical bonding data of each bonding component; the movement record provides a basis for subsequent data analysis and model building.

[0098] It is worth noting that the visual recognition system includes a plurality of visual recognition modules and a data processing module, wherein the visual recognition module is arranged on the movement path of the mobile device to monitor the data of the mobile device; the data processing module receives various data transmitted by all visual recognition modules, obtains and stores the size data of the parts to be bonded after unified processing, and the size data includes the type, size, edge protrusion value of the parts to be monitored, and also includes the standard moving distance of the parts to be bonded; the visual recognition module includes a first type of visual recognition module arranged on the bonding device for identifying the edge of the parts to be bonded, and also includes a second type of visual recognition module arranged on the bonding device for identifying the displacement of the bracket, and also includes a feedback system connecting the first type of visual recognition module and the second type of visual recognition module; the first type of visual recognition module includes a visual recognition submodule, a visual recognition processor and a first output submodule connected in sequence; the visual recognition submodule is used to obtain the image data of the parts to be bonded; the visual recognition processor is used to process the image data; the first output submodule is used to output the recognition information; the edge state of the parts to be bonded can be identified by the first type of visual recognition module, the displacement of the bonding parts can be monitored by the second type of visual recognition module, and the bonding quality of the parts to be bonded can be judged by comparing the edge state and the displacement. The feedback system includes a sensor plate arranged at the connection between the first type of visual recognition module and the second type of visual recognition module; the sensor plate controls the second type of visual recognition module according to the state of the first type of visual recognition module; the sensor plate can make the first type of visual recognition module and the second type of visual recognition module work at the same time.

[0099] In step S12, the X-axis offset degree and the Y-axis offset degree of each of the bonding components are calculated based on the standard moving speed and the standard moving position.

[0100] It is worth noting that in the automated bonding equipment, the X-axis offset and the Y-axis offset are key features for measuring the motion accuracy of the bonding component. These features describe the deviation of the bonding component from the predetermined trajectory during the motion process. The X-axis offset is the ratio of the difference between the standard moving speed and the moving speed of the bonding component to the standard moving speed, and the Y-axis offset is the ratio of the difference between the standard moving position and the moving position of the bonding component to the standard moving position. The bonding trajectory model in the bonding model is determined by these two offsets.

[0101] In step S13, a fitting operation is performed using the least square method according to the preset first model parameters, the preset second model parameters, the size data of the fitting component and the historical fitting data to obtain the fitting first model parameters and the fitting second model parameters.

[0102] The preset first model parameters include the X-axis offset degree and the Y-axis offset degree, which are used to construct a fitting trajectory model; the preset second model parameters include the force applied by the fitting component and the distance between the fitting component and the component to be fitted, which are used to construct a fitting force model; the dimension data of the fitting component includes the type and size of the fitting component;

[0103] It is worth noting that, according to the preset first model parameters, the preset second model parameters and the size data of the fitting component, a multivariate linear regression model is defined to combine the preset model parameters with the actual data. The formula of the multivariate linear regression model is as follows:

[0104] Y=a 0 +(a x +a y +β)·s+∈;

[0105] Among them, a 0 is a constant, a x and a y is the X-axis offset degree and the Y-axis offset degree in the preset first model parameters, β is the pressure coefficient obtained by fitting the second model parameters, s is the sum of the overall width, overall height and maximum thickness in the dimension data of the bonding component, ∈ is the error term; ∈ is assumed to be random noise, which is an independent and identically distributed random variable with a constant variance, that is, ∈~N(0, σ 2 ), σ 2 is the error term variance.

[0106] Furthermore, the least square method is implemented by minimizing the sum of squares of the errors between the predicted value and the actual value, and the formula is as follows:

[0107]

[0108] Wherein, Yi is the actual bonding gap size in the historical bonding data, is the predicted fit gap size, Calculated from the multiple linear regression model, m is the total number of observations.

[0109] It is worth noting that in this fitting process, the parameter a that minimizes the S value is x 、a y is the first model parameter, and parameter β is a comprehensive reflection of the two parameters in the second model parameter. The first model parameter and the second model parameter need to be further optimized to construct a better fitting model.

[0110] In step S14, a genetic algorithm is used to search for the optimal first model parameter and the optimal second model parameter according to the fitted first model parameter and the fitted second model parameter to obtain the optimal first model parameter and the optimal second model parameter.

[0111] It is worth noting that the genetic algorithm is a search algorithm that simulates the principles of natural selection and genetics and is used to solve optimization and search problems. It works by iteratively improving a population of candidate solutions, which are called individuals in each iteration of the algorithm. Each individual has a value associated with fitness, which measures the individual's ability to solve the problem.

[0112] Specifically, according to the first model fitting parameters and the second model fitting parameters, random perturbations are introduced to generate multiple sets of parameters as the initial population of the genetic algorithm. Here, 10 sets of parameters are generated, and each set has a small random perturbation relative to the fitting parameters. The random perturbation is introduced by adding a normally distributed random number with a mean of 0 and a standard deviation of 0.01. The fitness of each individual in the initial population is calculated to obtain the first-generation fitness. The fitness function is defined as the sum of squared errors between the historical fitting data and the fitting model prediction. The specific formula of the fitness function is as follows:

[0113]

[0114] Among them, j∈{1, 2, ..., m}, m is the total number of generated individuals, F j is the fitness of the jth individual, y i is the fitting position in the historical fitting data, is the fitting position predicted by the fitting model, F i is the fitting force in the historical fitting data, The fitting force predicted by the fitting model;

[0115] Furthermore, according to the first generation fitness, select 5 individuals with the highest fitness from these 10 individuals, each individual has three parameters: X-axis offset, Y-axis offset, and pressure coefficient; and set the fitness of the individual with the highest fitness as the first fitness; randomly pair these 5 individuals, and exchange some of their parameters with a probability of 1%, and the mutation is achieved by adding a normally distributed random number with a mean of 0 and a standard deviation of 1% to the parameters. Repeat the above selection, crossover, and mutation steps to determine whether the fitness of each subsequent generation of individuals is better than the first fitness. If so, update the fitness of the new generation of individuals to the first fitness; if not, discard the fitness of the generation of individuals; when the number of iterations is greater than or equal to 50, output the current first fitness as the optimal fitness;

[0116] The individual parameters corresponding to the optimal fitness include an optimal first model parameter and an optimal second model parameter, and a more accurate fitting model is constructed by using the optimal first model parameter and the optimal second model parameter.

[0117] In step S15, a fitting model is constructed according to the optimal first model parameters and the optimal second model parameters.

[0118] It is worth noting that, according to the optimal first model parameters, a fitting trajectory model is constructed, and the formula of the fitting trajectory model is as follows:

[0119]

[0120] in, is the position vector of the bonding component at time t, including the positions of the X-axis and the Y-axis, is the initial velocity vector of the fitted component, is the acceleration vector of the fitted component, α X and α Y are the X-axis offset degree and the Y-axis offset degree, respectively, and are the X-axis and Y-axis coordinates of the initial position of the bonding component, and are the X-axis and Y-axis coordinates of the origin of the moving coordinate system, and t is the moving time. The bonding trajectory model is used to describe the motion trajectory of the bonding component during the automated bonding process. This model is usually based on the laws of physics and the principles of mechanical motion, and takes into account the initial position, speed, acceleration, and displacement of the bonding component during the motion process. According to the bonding trajectory model, the position of the bonding component at any point in time can be predicted to ensure that the component can be accurately moved to the predetermined position; the model can also be used to plan the moving path of the bonding component to avoid collisions and unnecessary movements and improve bonding efficiency.

[0121] Furthermore, according to the optimal second model parameters, a fitting force model is constructed, and the formula of the fitting force model is as follows:

[0122]

[0123] Among them, β is the pressure coefficient, f is the force applied by the bonding component, and d is the distance between the bonding component and the component to be bonded. The bonding force model is used to describe the force applied by the bonding component during the bonding process and the interaction force between the bonding component and the component to be bonded. This model takes into account the physical properties of the bonding component and the material properties of the component to be bonded. Through this model, it can help control the force applied during the bonding process to ensure that the bonding force is neither too large to cause material damage nor too small to cause loose bonding, while avoiding the generation of bubbles and wrinkles.

[0124] In step S16, the bonding model is input into the bonding model for fitting operation according to the size data of the component to be bonded, the optimal first model parameters and the optimal second model parameters, so as to obtain a bonding solution.

[0125] The bonding scheme includes the motion parameters, motion time, moving distance, moving time and bonding force of the bonding component to be bonded to the component to be bonded; when the bonding component is a roller, the motion parameters include the rotation angle, rotation speed and radius of the roller; when the bonding component is a clamp, the motion parameters include the rotation angle, rotation acceleration and radius. The motion time refers to the time required for the bonding component to perform a specific action, such as the time required for a roller to complete one rotation.

[0126] Exemplarily, when the bonding component is a roller, the angle that the roller needs to rotate is calculated based on the initial position and target position of the roller; the rotation speed is the rate of change of the roller rotation angle over time; the roller radius is a geometric characteristic given by the design parameters of the equipment, and during the bonding process, the roller radius will affect the contact area between the bonding component and the component to be bonded. When the bonding component is a splint, the rotation angle and radius of the splint are obtained in the same way as the roller; the rotation acceleration of the splint is the rate of change of the splint angular velocity over time. The moving distance is determined based on the starting position and the ending position of the bonding component, and the bonding force is calculated based on the moving distance and the pressure coefficient obtained from the optimal second model parameter.

[0127] In step S17, the edge protrusion value and the offset value are calculated according to the edge protrusion distance of the component to be bonded, the overall width of the component to be bonded, the standard moving speed of the component to be bonded, and the actual moving speed of the component to be bonded.

[0128] It should be noted that the edge protrusion distance of the component to be bonded refers to the distance that the edge of the component to be bonded protrudes or sinks relative to the bonding component or base during the bonding process. This parameter is crucial to ensure the correct alignment and bonding quality between the bonding component and the component to be bonded. The ratio of the edge protrusion distance of the component to be bonded to the overall width of the component to be bonded is calculated to obtain the edge protrusion value, and the calculation formula of the edge protrusion value is as follows:

[0129]

[0130] Wherein, EF is the edge protrusion value, E is the edge protrusion distance of the component to be bonded, and W is the overall width of the component to be bonded;

[0131] Furthermore, the standard moving speed of the bonding component is subtracted from the actual moving speed of the bonding component to obtain a moving speed difference; the proportion of the moving speed difference to the standard moving speed of the bonding component is calculated to obtain a deviation value, and the calculation formula of the deviation value is as follows:

[0132]

[0133] Among them, SD is the deviation value, V s is the standard moving speed, V a is the actual moving speed.

[0134] In step S18, the edge protrusion value and the offset degree value are compared with the preset edge protrusion value threshold and the preset offset degree value threshold respectively to obtain a comparison result; if the comparison result is normal, normal operation is performed; if the comparison result is abnormal, no fitting solution is output.

[0135] It is worth noting that the preset edge protrusion value threshold refers to the maximum protrusion or depression distance of the edge of the part to be bonded relative to the bonding part or the base allowed during the bonding process. This threshold is set according to the size, material properties, and bonding process requirements of the bonding part and the part to be bonded. It ensures that the bonding part and the part to be bonded can be correctly aligned and have sufficient bonding strength. The present invention sets the preset edge protrusion value threshold to 5% of the overall width of the part to be bonded. The preset offset degree value threshold refers to the maximum speed deviation ratio allowed during the movement of the bonding part. This threshold is usually determined based on the performance parameters of the bonding equipment and historical bonding data to ensure the movement accuracy and stability during the bonding process. The present invention sets the preset offset degree value threshold to ±2% of the standard moving speed of the bonding part.

[0136] Furthermore, if the edge protrusion value is less than or equal to the preset edge protrusion value threshold, and the offset degree value does not exceed the preset offset degree value, both conditions are met and the fitting solution can be output normally; if any of the above conditions is not met, it is considered that there is an abnormality and no fitting solution is output.

[0137] In step S19, according to the bonding scheme, the movement of the bonding component is controlled to complete the bonding operation of the bonding component and the component to be bonded.

[0138] It should be noted that before performing the bonding operation according to the bonding scheme, the bonding component needs to be moved to the initial position, and the initial position is determined by the control system of the bonding equipment according to a pre-set program; according to the bonding scheme, the control system will send instructions to the driving system of the bonding component to control the bonding component to move according to the required moving speed and moving position parameters; at the same time, a certain force needs to be applied during the bonding process between the bonding component and the component to be bonded, and this bonding force is controlled according to the requirements in the bonding scheme. The control of the bonding force can be monitored in real time by a pressure sensor and fed back to the control system for adjustment. After the bonding component moves to the target position and the bonding force is applied, the first bonding result is obtained.

[0139] Furthermore, the image algorithm Yolov5 is used to detect whether there are defects in the bonding area. Defect detection includes detecting whether there are bubbles, foreign matter, and inaccurate alignment. If the image algorithm detects that there are defects in the bonding area, the control system will adjust the bonding plan according to the detection results. For example, if bubbles are detected, the system will increase the bonding force or adjust the bonding speed.

[0140] In summary, the present invention provides a control method for an automated laminating device, comprising recording movement data of a plurality of laminating components in the laminating device by means of image acquisition and position sensing according to the laminating operation of the laminating device to obtain a movement record; wherein the laminating components include rollers, clamps, a bracket connected to the laminating device, and a visual recognition system, and the movement record includes historical laminating data of each laminating component; obtaining a standard movement speed and a standard movement position of the laminating component, and calculating an X-axis offset degree and a Y-axis offset degree of each laminating component according to the standard movement speed and the standard movement position; obtaining The dimension data of the bonding component is obtained, and a fitting operation is performed using the least square method according to the preset first model parameters, the preset second model parameters, the dimension data of the bonding component and the historical bonding data to obtain the fitting first model parameters and the fitting second model parameters; wherein the dimension data of the bonding component includes the type and size of the bonding component; the preset first model parameters include the X-axis offset degree and the Y-axis offset degree, and the preset second model parameters include the force applied by the bonding component and the distance between the bonding component and the component to be bonded, and the component to be bonded includes a display screen, a touch screen, a double-sided adhesive tape and a decorative surface. board; according to the fitting first model parameters and the fitting second model parameters, a genetic algorithm is used to search for the best first model parameters and the best second model parameters; according to the best first model parameters and the best second model parameters, a fitting model is constructed; the size data of the parts to be bonded are obtained, and according to the size data of the parts to be bonded, the best first model parameters and the best second model parameters, the bonding model is input to perform a fitting operation to obtain a bonding solution; wherein the size data of the parts to be bonded include the type, size, edge protrusion distance and standard moving distance of the parts to be bonded, and the size data of the parts to be bonded The overall width and overall height of the component to be bonded; according to the edge protrusion distance of the component to be bonded, the overall width of the component to be bonded, the standard moving speed of the bonding component and the actual moving speed of the bonding component, the edge protrusion value and the offset degree value are calculated; the edge protrusion value and the offset degree value are compared with the preset edge protrusion value threshold and the preset offset degree value threshold respectively to obtain a comparison result; if the comparison result is normal, normal operation is performed, and if the comparison result is abnormal, no bonding plan is output; according to the bonding plan, the movement of the bonding component is controlled to complete the bonding operation of the bonding component and the component to be bonded.

[0141] The present invention provides a control method for an automated laminating device. First, a mobile record acquisition module is used to collect in real time the movement data of multiple laminating components in the laminating device, including the historical laminating data of a roller, a clamping plate, a bracket connected to the laminating device, and a visual recognition system. Then, the X-axis offset degree and the Y-axis offset degree of each laminating component are calculated by an offset degree calculation module. Then, these data and the preset first model parameters and second model parameters are used to perform a fitting operation by the least squares method to obtain the fitted first model parameters and the fitted second model parameters. In addition, the present invention also uses a genetic algorithm to optimize the model parameters obtained by fitting to obtain the optimal first model parameters and the optimal second model parameters. According to these optimal model parameters, a laminating model is constructed, and the size data of the components to be laminating are obtained, including the type, size, edge protrusion value, and standard moving distance. Then, according to the size data of the components to be laminating, the optimal first model parameters, and the optimal second model parameters, the laminating model is input for fitting operation to obtain a laminating solution. The present invention further obtains the edge protrusion distance and the overall width of the component to be bonded, calculates the edge protrusion value and the offset value, and compares them with the preset threshold value to determine whether it is operating normally or outputting no bonding solution. Finally, according to the bonding solution, the movement of the bonding component is controlled to complete the bonding operation of the bonding component and the component to be bonded. Through this series of operations, the present invention provides an intelligent and automated bonding equipment control solution, which greatly improves the bonding accuracy and efficiency, ensures product quality, and reduces uncertainty and waste in production.

[0142] The present invention provides a control method, system, device and storage medium for automated laminating equipment to solve the problem of low laminating quality between a laminating component and a component to be laminarized, and to achieve automated laminating with high efficiency and high precision.

[0143] Reference Figure 2 The second embodiment of the present invention provides a control system for an automated laminating device, comprising:

[0144] The movement record acquisition module 100 is used to record the movement data of multiple bonding components in the bonding device by image acquisition and position sensing according to the bonding operation of the bonding device to obtain the movement record; wherein the bonding components include rollers, clamps, brackets connected to the bonding device and a visual recognition system, and the movement record includes the historical bonding data of each bonding component;

[0145] The standard data acquisition module 101 is used to acquire the standard moving speed and standard moving position of the bonding component;

[0146] The offset degree calculation module 102 is used to calculate the X-axis offset degree and the Y-axis offset degree of each of the bonding components according to the standard moving speed and the standard moving position;

[0147] The fitting component size data acquisition module 103 is used to acquire the size data of the fitting component; wherein the size data of the fitting component includes the type and size of the fitting component;

[0148] The fitting model parameter acquisition module 104 is used to perform a fitting operation using the least square method according to the preset first model parameters, the preset second model parameters, the size data of the bonding component and the historical bonding data to obtain the fitting first model parameters and the fitting second model parameters; wherein the preset first model parameters include the X-axis offset degree and the Y-axis offset degree, and the preset second model parameters include the force applied by the bonding component and the distance between the bonding component and the component to be bonded, and the component to be bonded includes a display screen, a touch screen, a double-sided adhesive tape and a decorative panel;

[0149] An optimal model parameter acquisition module 105 is used to use a genetic algorithm to perform optimization according to the fitted first model parameter and the fitted second model parameter to obtain an optimal first model parameter and an optimal second model parameter;

[0150] A fitting model building module 106, configured to build a fitting model according to the optimal first model parameter and the optimal second model parameter;

[0151] The module 107 for acquiring the dimension data of the component to be bonded is used to acquire the dimension data of the component to be bonded; wherein the dimension data of the component to be bonded includes the type, size, edge protrusion distance and standard moving distance of the component to be bonded, and the overall width and overall height of the component to be bonded;

[0152] The fitting scheme acquisition module 108 inputs the fitting model to perform fitting operation according to the size data of the component to be fitted, the first fitting model parameter and the second fitting model parameter, and obtains the fitting scheme; calculates the edge protrusion value and the offset value according to the edge protrusion distance of the component to be fitted, the overall width of the component to be fitted, the standard moving speed of the fitting component and the actual moving speed of the fitting component; compares the edge protrusion value and the offset value with a preset edge protrusion value threshold and a preset offset value threshold, respectively, to obtain a comparison result; if the comparison result is normal, the system operates normally; if the comparison result is abnormal, the system outputs that there is no fitting scheme;

[0153] The bonding operation module 109 is used to control the movement of the bonding component according to the bonding plan to complete the bonding operation of the bonding component and the component to be bonded.

[0154] It should be noted that the control system of an automated laminating device provided in an embodiment of the present invention is used to execute all process steps of a control method of an automated laminating device in the above embodiment, and the working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0155] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the control method of the above-mentioned automatic bonding device are implemented, for example Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above system embodiment are realized, such as the optimal model parameter acquisition module.

[0156] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0157] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0158] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0159] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0160] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0161] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0162] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A control method for an automated laminating device, characterized in that: include: According to the laminating operation of the laminating device, the movement data of multiple laminating components in the laminating device are recorded by image acquisition and position sensing to obtain a movement record; wherein the laminating components include rollers, clamps, brackets connected to the laminating device and a visual recognition system, and the movement record includes historical laminating data of each laminating component; Obtaining a standard moving speed and a standard moving position of the bonding component, and calculating an X-axis offset degree and a Y-axis offset degree of each of the bonding components according to the standard moving speed and the standard moving position; Acquire the dimension data of the bonding component, and perform fitting operation using the least square method according to the preset first model parameters, the preset second model parameters, the dimension data of the bonding component and the historical bonding data to obtain the fitted first model parameters and the fitted second model parameters; wherein the dimension data of the bonding component includes the type and size of the bonding component; the preset first model parameters include the X-axis offset degree and the Y-axis offset degree, and the preset second model parameters include the force applied by the bonding component and the distance between the bonding component and the component to be bonded, and the component to be bonded includes a display screen, a touch screen, a double-sided tape and a decorative panel; According to the fitted first model parameters and the fitted second model parameters, a genetic algorithm is used to perform optimization to obtain an optimal first model parameter and an optimal second model parameter; Constructing a fitting model according to the optimal first model parameter and the optimal second model parameter; Acquire the size data of the component to be bonded, and input the bonding model to perform a fitting operation according to the size data of the component to be bonded, the optimal first model parameter, and the optimal second model parameter, so as to obtain a bonding solution; wherein the size data of the component to be bonded includes the type, size, edge protrusion distance, and standard moving distance of the component to be bonded, as well as the overall width and overall height of the component to be bonded; Calculate the edge protrusion value and the offset value according to the edge protrusion distance of the component to be bonded, the overall width of the component to be bonded, the standard moving speed of the component to be bonded, and the actual moving speed of the component to be bonded; Compare the edge protrusion value and the offset value with a preset edge protrusion value threshold and a preset offset value threshold, respectively, to obtain a comparison result; if the comparison result is normal, then the operation is performed normally; if the comparison result is abnormal, then output a no-fitting solution; According to the bonding scheme, controlling the movement of the bonding component to complete the bonding operation of the bonding component and the component to be bonded; The X-axis offset degree and the Y-axis offset degree of each of the bonding components are calculated according to the standard moving speed and the standard moving position, including: Acquiring the moving speed of the laminating component; Performing a subtraction operation according to the standard moving speed and the moving speed of the bonding component to obtain a speed difference; Calculate the proportion of the speed difference in the standard moving speed to obtain the X-axis offset degree of the bonding component; Acquiring the moving position of the laminating component; Performing a subtraction operation based on the standard moving position and the moving position of the fitting component to obtain a position difference; Calculate the proportion of the position difference in the standard moving position to obtain the Y-axis offset degree of the fitting component; According to the preset first model parameters, the preset second model parameters, the size data of the bonding component and the historical bonding data, a fitting operation is performed using the least square method to obtain the fitting first model parameters and the fitting second model parameters, including: A multivariate linear regression model is defined according to the preset first model parameters, the preset second model parameters and the size data of the fitting component, and the formula is as follows: ;in, is a constant, and is the X-axis offset degree and the Y-axis offset degree in the preset first model parameter, is the pressure coefficient obtained by fitting the second model parameters, is the size of the fitting component, is the error term; the least squares method is used to perform the fitting operation to obtain the first model parameter and the second model parameter; wherein the least squares method is implemented by minimizing the sum of squares of the errors between the predicted value and the actual value, and the formula is as follows: ;in, is the actual bonding gap size in the historical bonding data, is the predicted fit gap size, Calculated by the multivariate linear regression model, m is the total number of observations; wherein the bonding scheme includes: the bonding scheme is the motion parameters, motion time, moving distance, moving time and bonding force of the bonding component to be bonded to the component to be bonded; When the fitting component is a roller, the motion parameters include the rotation angle, rotation speed and radius of the roller; when the fitting component is a clamping plate, the motion parameters include the rotation angle, rotation acceleration and radius.

2. The control method of the automatic laminating equipment according to claim 1, characterized in that: The method of using a genetic algorithm to search for an optimal first model parameter and an optimal second model parameter according to the fitted first model parameter and the fitted second model parameter comprises: According to the fitted first model parameters and the fitted second model parameters, random disturbances are introduced to generate multiple groups of parameters as initial populations of the genetic algorithm; The fitness of each individual in the initial population is calculated to obtain the first generation fitness. The fitness function is defined as the sum of squares of the errors between the historical fitting data and the fitting model prediction. The specific formula of the fitness function is as follows: ;in, , m is the total number of generated individuals, is the fitness of the jth individual, is the fitting position in the historical fitting data, is the fitting position predicted by the fitting model, is the fitting force in the historical fitting data, is the fitting force predicted by the fitting model; according to the first-generation fitness, a preset number of individuals are selected from a plurality of individuals in a descending order of fitness to perform random pairing and gene exchange, the value of a certain parameter is changed with a preset probability, and the fitness of the individual with the highest fitness is set as the first fitness; Iterate according to the fitness function to obtain the fitness of the new generation of individuals, and record the number of iterations; Determine whether the fitness of the new generation of individuals is better than the first fitness, if so, update the fitness of the new generation of individuals to the first fitness; if not, discard the fitness of the first generation of individuals; Continue to the next iteration, when the number of iterations is greater than or equal to the preset number of iterations, output the current first fitness as the optimal fitness; The individual parameters corresponding to the optimal fitness include the optimal first model parameters and the optimal second model parameters.

3. The control method of the automatic laminating equipment according to claim 1, characterized in that: The step of constructing a fitting model according to the optimal first model parameter and the optimal second model parameter comprises: According to the optimal first model parameters, a fitting trajectory model is constructed, and the formula of the fitting trajectory model is as follows: ;in, is the position vector of the bonding component at time t, including the positions of the X-axis and the Y-axis, is the initial velocity vector of the fitted component, is the acceleration vector of the fitted component, and are the X-axis offset degree and the Y-axis offset degree, respectively, and are the X-axis and Y-axis coordinates of the initial position of the bonding component, and are the X-axis and Y-axis coordinates of the origin of the moving coordinate system, and t is the moving time; According to the optimal second model parameters, a fitting force model is constructed, and the formula of the fitting force model is as follows: ;in, is the pressure coefficient, f is the force applied by the bonding component, and d is the distance between the bonding component and the component to be bonded; Wherein, the fitting model includes the fitting trajectory model and the fitting force model.

4. The control method of the automatic laminating equipment according to claim 1, characterized in that: The edge protrusion value and the offset degree value are calculated based on the edge protrusion distance and the overall width of the component to be bonded and the standard moving speed and the actual moving speed of the bonding component, including: The ratio of the edge protrusion distance of the component to be bonded to the overall width of the component to be bonded is calculated to obtain the edge protrusion value. The calculation formula of the edge protrusion value is as follows: ;in, is the edge protrusion value, E is the edge protrusion distance of the component to be bonded, and W is the overall width of the component to be bonded; Subtracting the standard moving speed of the laminating component from the actual moving speed of the laminating component to obtain a moving speed difference; The proportion of the moving speed difference to the standard moving speed of the bonding component is calculated to obtain the offset value. The calculation formula of the offset value is as follows: ;in, is the offset value, is the standard moving speed, is the actual moving speed.

5. The control method of the automatic laminating equipment according to claim 1, characterized in that: According to the bonding scheme, the movement of the bonding component is controlled to complete the bonding operation of the bonding component and the component to be bonded, including: Moving the fitting component to an initial position; According to the moving speed and moving position parameters of the bonding component required in the bonding scheme, the bonding component is controlled to move, and the bonding component and the component to be bonded are bonded according to the bonding force requirements of the bonding scheme to obtain a first bonding result; According to the first bonding result, detecting whether there is a defect in the bonding area by an image algorithm to obtain a defect detection result; if the defect detection result is abnormal, adjusting the bonding plan to obtain a second bonding plan; if the defect detection result is no abnormality, the bonding plan is the second bonding plan; According to the second bonding scheme, the bonding component is controlled to move to obtain a second bonding result, thereby completing the bonding operation of the bonding component and the component to be bonded.

6. A control system for an automated laminating device, used to implement the method for an automated laminating device as claimed in any one of claims 1 to 5, characterized in that: include: A movement record acquisition module, used to record movement data of a plurality of laminating components in the laminating device by means of image acquisition and position sensing according to the laminating operation of the laminating device, so as to obtain movement records; wherein the laminating components include rollers, clamps, brackets connected to the laminating device and a visual recognition system, and the movement records include historical laminating data of each laminating component; A standard data acquisition module, used to acquire a standard moving speed and a standard moving position of the bonding component; A deviation degree calculation module, used for calculating the X-axis deviation degree and the Y-axis deviation degree of each of the bonding components according to the standard moving speed and the standard moving position; A fitting component size data acquisition module, used to acquire the size data of the fitting component; wherein the size data of the fitting component includes the type and size of the fitting component; A fitting model parameter acquisition module, used to perform a fitting operation using the least square method according to a preset first model parameter, a preset second model parameter, the size data of the bonding component and the historical bonding data, to obtain a fitting first model parameter and a fitting second model parameter; wherein the preset first model parameter includes the X-axis offset degree and the Y-axis offset degree, and the preset second model parameter includes the force applied by the bonding component and the distance between the bonding component and a component to be bonded, and the component to be bonded includes a display screen, a touch screen, a double-sided adhesive tape and a decorative panel; An optimal model parameter acquisition module, used to use a genetic algorithm to perform optimization according to the fitted first model parameter and the fitted second model parameter to obtain an optimal first model parameter and an optimal second model parameter; A fitting model construction module, used to construct a fitting model according to the optimal first model parameter and the optimal second model parameter; A module for acquiring the size data of the parts to be bonded, used for acquiring the size data of the parts to be bonded; wherein the size data of the parts to be bonded include the type, size, edge protrusion distance and standard moving distance of the parts to be bonded, and the overall width and overall height of the parts to be bonded; A fitting scheme acquisition module is configured to input the fitting model to perform fitting operation according to the size data of the component to be fitted, the first fitting model parameter and the second fitting model parameter, so as to obtain a fitting scheme; calculate an edge protrusion value and an offset value according to the edge protrusion distance of the component to be fitted, the overall width of the component to be fitted, the standard moving speed of the fitting component and the actual moving speed of the fitting component; compare the edge protrusion value and the offset value with a preset edge protrusion value threshold and a preset offset value threshold, respectively, to obtain a comparison result; if the comparison result is normal, the module operates normally; if the comparison result is abnormal, the module outputs that no fitting scheme is available; The laminating operation module is used to control the movement of the laminating component according to the laminating scheme, so as to complete the laminating operation of the laminating component and the component to be laminarized.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the control method of the automated bonding device according to any one of claims 1 to 5.

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