Capacitive touch screen conductive film bonding process optimization method, device and storage medium

Abstract

The application discloses a capacitive touch screen conductive film lamination process optimization method, equipment and storage medium, relates to the touch screen manufacturing technical field, and the method comprises the following steps: based on the material parameters of the capacitive touch screen conductive film, calibrating the lamination equipment and generating initial lamination parameters, using an adaptive control model for lamination prediction and dynamic optimization, formulating and executing a lamination control strategy to complete the accurate lamination of the conductive film. Through quality detection, product data is obtained, and the detection result is fed back to the model to optimize the lamination parameters, so that intelligent dynamic adjustment and continuous improvement of the lamination process are realized. The technical problems of uneven lamination and poor waterproof and dustproof performance caused by the fact that the traditional frame lamination method cannot dynamically adjust parameters in the existing capacitive touch screen conductive film lamination process are solved, and the technical effects of improving the lamination uniformity and waterproof and dustproof performance of the capacitive touch screen conductive film are achieved.

Description

Technical Field

[0001] This application relates to the field of touch screen manufacturing technology, and in particular to a method, equipment and storage medium for optimizing the conductive film bonding process of capacitive touch screens. Background Technology

[0002] With the trend towards thinner, lighter, and more reliable electronic products, capacitive touchscreens are widely used in various devices, making the conductive film lamination process a crucial step in touchscreen manufacturing. Traditional frame-mount lamination, due to fixed parameters during the lamination process, struggles to guarantee lamination accuracy and uniformity, easily leading to gaps between the conductive film and the substrate, thus reducing the product's waterproof and dustproof performance. This process is also more prone to moisture and dust penetration in harsh environments, affecting touch sensitivity and product lifespan. Furthermore, traditional frame-mount lamination lacks real-time parameter adjustment and finished product feedback mechanisms, resulting in poor adaptability to different materials and lamination conditions, and difficulty in meeting the high-performance requirements of modern products in terms of process consistency and stability. Therefore, overcoming the limitations of frame-mount lamination, combining it with full lamination technology, optimizing pressure, temperature, and speed control during the lamination process, and improving lamination quality and environmental adaptability have become urgent problems for the industry to solve.

[0003] Currently, in the relevant technologies, there are technical problems in the traditional frame bonding method of capacitive touch screen conductive film bonding process, which suffers from uneven bonding and poor waterproof and dustproof performance due to the inability to dynamically adjust parameters. Summary of the Invention

[0004] This application provides a method, equipment, and storage medium for optimizing the bonding process of conductive films for capacitive touchscreens. It utilizes material parameters based on the conductive film of the capacitive touchscreen, calibrates the bonding equipment and generates initial bonding parameters, employs an adaptive control model for bonding prediction and dynamic optimization, and formulates and executes a bonding control strategy to achieve precise bonding of the conductive film. Finished product data is obtained through quality inspection, and the inspection results are fed back to the model to optimize the bonding parameters, achieving intelligent dynamic adjustment and continuous improvement of the bonding process. This results in improved uniformity of conductive film bonding and enhanced waterproof and dustproof performance for capacitive touchscreens.

[0005] This application provides an optimized method for bonding conductive films in capacitive touchscreens, including:

[0006] Based on the fundamental material parameters of the capacitive touchscreen conductive film, the bonding equipment is calibrated to generate multiple initial bonding parameters. An adaptive control model is constructed, and the multiple initial bonding parameters are synchronized to the adaptive control model to perform bonding predictions according to the bonding process parameters of the capacitive touchscreen conductive film, obtaining multiple bonding control prediction information. The bonding dynamics of the capacitive touchscreen conductive film are dynamically optimized according to the multiple bonding control prediction information to formulate a bonding control strategy. The bonding control strategy is executed to position and bond the capacitive touchscreen conductive film, generating bonded product information. The bonded product information is then subjected to quality inspection to obtain finished product quality inspection data. The finished product quality inspection data is fed back to the adaptive control model for response, generating an adaptive control optimization model. The bonding process parameters of the capacitive touchscreen conductive film are intelligently optimized and updated through the adaptive control optimization model.

[0007] This application also provides an electronic device, including:

[0008] A memory is used to store executable instructions; a processor is used to implement an optimized method for bonding conductive films in a capacitive touchscreen when executing the executable instructions stored in the memory.

[0009] This application also provides a computer-readable storage medium, comprising:

[0010] It stores a computer program that, when executed by a processor, implements an optimization method for the bonding process of conductive films in capacitive touchscreens.

[0011] The proposed optimization method, equipment, and storage medium for capacitive touchscreen conductive film lamination process, as outlined in this application, firstly calibrates the lamination equipment and generates initial lamination parameters based on the material parameters of the capacitive touchscreen conductive film. Then, an adaptive control model is used for lamination prediction and dynamic optimization, and a lamination control strategy is formulated and executed to achieve precise lamination of the conductive film. Finished product data is obtained through quality inspection, and the inspection results are fed back to the model to optimize the lamination parameters, achieving intelligent dynamic adjustment and continuous improvement of the lamination process. This results in improved uniformity of capacitive touchscreen conductive film lamination and enhanced waterproof and dustproof performance. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0013] Figure 1 A flowchart illustrating the optimized method for bonding conductive film to a capacitive touchscreen according to an embodiment of this application;

[0014] Figure 2 This is a schematic diagram of the adaptive control model obtained by the capacitive touch screen conductive film bonding process optimization method provided in the embodiments of this application.

[0015] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0016] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0019] This application provides an optimized method for bonding conductive films in capacitive touchscreens, such as... Figure 1 As shown, the method includes:

[0020] Step S100 involves calibrating the bonding equipment based on the fundamental material parameters of the capacitive touchscreen conductive film, generating multiple initial bonding parameters. Specifically, the capacitive touchscreen conductive film possesses various fundamental material parameters, including physical parameters (such as thickness, hardness, elastic modulus, and coefficient of thermal expansion), electrical parameters (such as conductivity and capacitance), and chemical parameters (such as surface energy and chemical activity). After acquiring and analyzing these parameters, the bonding equipment is calibrated according to the characteristics of the conductive film. For pressure calibration, the pressure system of the bonding equipment is adjusted based on the hardness, elastic modulus, and thickness of the conductive film, including the pressure upper limit, pressure sensor, pressure control mechanism, and pressure distribution system, to ensure sufficient and uniform pressure during bonding. Temperature calibration is performed based on the coefficient of thermal expansion and bonding process requirements to determine the bonding temperature range, and the temperature sensor and heating element are calibrated to ensure temperature stability and uniformity. Speed ​​calibration considers the material characteristics of the conductive film, adjusting the moving speed of the bonding equipment according to its hardness and bonding accuracy requirements or flexibility and production efficiency requirements. Through these calibration processes, multiple initial bonding parameters are ultimately generated, including pressure value (numerical range or pressure curve), bonding temperature (initial temperature, fluctuation range, temperature gradient control), bonding speed (specific value or speed change curve), bonding time, vacuum degree, etc.

[0021] Step S200: Construct an adaptive control model. Synchronize the multiple initial bonding parameters to the adaptive control model and predict bonding according to the bonding process parameters of the capacitive touchscreen conductive film to obtain multiple bonding control prediction information. Specifically, constructing the adaptive control model requires selecting a suitable structure (such as neural networks, fuzzy logic control, etc.) based on the characteristics of the capacitive touchscreen conductive film bonding process. Its design principle is to automatically adjust the control strategy based on input parameters and feedback information. During construction, the model parameters (such as neural network weights, thresholds, or fuzzy logic membership function parameters) are initialized. Initial values ​​can be determined based on experience or preset rules and adjusted subsequently. A dedicated parameter input interface is designed to synchronize multiple initial bonding parameters (such as pressure, temperature, speed, etc.) to the model. Before input, data preprocessing may be required, such as normalization to the [0, 1] interval to improve model training efficiency and accuracy. The bonding process parameters (such as pressure curves at different stages, temperature ranges, and the relationship between speed and time) are integrated with the initial bonding parameters as model inputs. A mathematical model of the bonding process is constructed using a specific algorithm. The model's built-in prediction algorithm (such as a neural network forward propagation algorithm) is used to predict the state of the conductive film at each moment during the bonding process (such as deformation and bonding quality in different areas). During the bonding process, high-sensitivity and fast-response sensors such as strain gauges and temperature sensor arrays are used to collect key information such as deformation and temperature distribution in real time. This information is fed back to the model to achieve closed-loop control. The model compares the prediction results with those of the initial parameters. If there is a deviation, the prediction results are modified or the control strategy is adjusted (such as adjusting the heater power or bonding speed) according to the adaptive adjustment mechanism. Finally, multiple bonding control prediction information is obtained to provide a basis for subsequent operations.

[0022] In one possible implementation, such as Figure 2As shown, an adaptive control model is constructed, and the multiple initial bonding parameters are synchronized to the adaptive control model to perform bonding prediction according to the bonding process parameters of the conductive film of the capacitive touch screen, thereby obtaining multiple bonding control prediction information. Step S200 further includes step S210, retrieving historical bonding data recording logs, and extracting multiple historical bonding parameters and multiple historical finished product quality data based on the historical bonding data recording logs. The multiple historical bonding parameters and the multiple historical finished product quality data have a corresponding relationship. Specifically, historical bonding data logs are retrieved from the storage system. These logs contain detailed information about the past bonding process of conductive films for capacitive touchscreens. The logs are processed to extract multiple historical bonding parameters, including bonding pressure values ​​(including pressure magnitude and rate of change at different stages), temperature settings (initial temperature, heating curve, constant temperature range, etc.), bonding speed (overall and speed adjustments for different bonding areas), and other relevant parameters of the bonding equipment (such as vacuum level and bonding time). Multiple historical finished product quality data are also extracted from these logs, including the electrical properties of the finished product (such as the conductivity of the conductive film, the capacitance value and stability of the touchscreen, etc.), physical properties (such as flatness after bonding, presence of bubbles or wrinkles, and the tightness of the film-substrate bonding), and appearance quality (whether there are scratches, impurities, etc.). The correspondence between historical bonding parameters and historical finished product quality data is clarified, that is, specific combinations of bonding parameters correspond to specific finished product quality conditions, providing a basis for subsequent analysis.

[0023] Step S220: An adaptive precision control algorithm is used to analyze the multiple historical bonding parameters and the multiple historical finished product quality data to determine multiple bonding-quality pairs. Specifically, an adaptive precision control algorithm is used to deeply analyze multiple historical bonding parameters and historical finished product quality data. The algorithm dynamically adjusts its analysis precision based on the data characteristics. Before analysis begins, the algorithm is initialized, including setting initial precision ranges, adjustment step sizes, and convergence criteria. These parameters are adaptively adjusted based on the actual data conditions during algorithm execution. Preprocessing of the extracted historical bonding parameters and historical finished product quality data is performed, including data normalization to map parameter values ​​of different magnitudes to a unified range, preventing unreasonable impacts from excessively large or small values. Feature extraction is then performed; for example, for bonding pressure parameters, features such as mean, variance, and rate of change can be extracted; for conductivity in finished product quality data, its fluctuation characteristics under different environments can be extracted. Preprocessing and feature extraction help the algorithm better uncover potential relationships within the data. Based on the data characteristics and the current analysis precision, the algorithm performs in-depth analysis of historical bonding parameters and historical finished product quality data. During the analysis, the algorithm's precision adjustment mechanism plays a crucial role. If the data changes relatively smoothly and the features are relatively simple, the algorithm will automatically improve the analysis accuracy, for example, by increasing the complexity of the fitting model or refining the data grouping. Conversely, if the data fluctuates greatly and the features are complex, the algorithm will appropriately reduce the accuracy and adopt an analysis model with greater generalization ability. Through adaptive analysis, the algorithm can accurately capture the correlation between historical bonding parameters and historical finished product quality data. Based on the analysis results, each set of related historical bonding parameters and historical finished product quality data is combined to form multiple bonding-quality pairs. For example, a specific set of bonding pressure, temperature, and speed parameters, along with the corresponding conductivity, flatness, and appearance quality evaluations, constitute a bonding-quality pair. The bonding-quality pairs comprehensively and accurately reflect the impact of different bonding conditions on the quality of finished products, providing key materials for subsequent dataset partitioning and model training.

[0024] Step S230: Determine the data training set, data supervision set, and data validation set according to the multiple fit-quality pairs. Specifically, the dataset is divided based on the determined multiple fit-quality pairs, following scientific and reasonable partitioning principles. The goal is to ensure that each dataset can play a specific role in the model training and evaluation process, while guaranteeing the independence and representativeness of the data. A portion of all fit-quality pairs is selected as the data training set. The selection process should consider the diversity and coverage of the data, including as many different types of fit situations and corresponding quality results as possible. For example, it should cover fit cases under different pressure ranges, temperature ranges, and speed combinations, as well as various finished product quality situations from high quality to low quality. This allows the training set to fully reflect the complex relationship between fit parameters and finished product quality, providing rich samples for model learning. A portion of the remaining fit-quality pairs is selected as the data supervision set. The role of the supervision set is to provide additional supervision information during model training. To help the model adjust its parameters and avoid overfitting, when selecting a supervision set, it is important to consider its complementarity with the training set. It should have a certain degree of similarity to ensure the effectiveness of supervision, while also having sufficient differences to fully leverage its supervisory role. For example, the supervision set can contain some fitting parameter combinations similar to those in the training set, but the corresponding finished product quality data may have subtle differences. This approach guides the model to learn more accurate relationships. The remaining fitting-quality pairs form the validation set. The validation set is mainly used to evaluate the model's generalization ability at different stages of model training. When selecting the validation set, it is crucial to ensure the independence of its data; that is, the fitting-quality pairs in the validation set should not have significant overlap with the data in the training and supervision sets. When the model performs well on the validation set, it can be confidently considered that the model has good generalization ability and can handle unseen new data.

[0025] Step S240: Supervised iterative comparative training is performed on the data training set according to the supervised data set to obtain multiple training results. Specifically, the data training set and the data supervised set are loaded into the training environment. The training environment is built based on a specific machine learning or data analysis framework and has the ability to process large-scale data and run complex model training algorithms. In this environment, the model structure for training is prepared. The model can be built based on algorithms such as neural networks, support vector machines, and decision trees. The specific model selection depends on the characteristics of the bonding process and the nature of the data. In each iteration of training, one or a set of bonding-quality pairs are selected from the data training set as input data and input into the model to be trained. The model predicts the corresponding finished product quality data based on the input historical bonding parameters. Then, the predicted results are compared with the corresponding actual finished product quality data in the data supervision set to calculate the error between the predicted and actual results. Various error measurement methods can be used, such as mean squared error (MSE) and mean absolute error (MAE). Based on the calculated error value, the model parameters are adjusted using the model's optimization algorithm. If it is a neural network model, the backpropagation algorithm is usually used to adjust the weights and biases in the network based on the error backpropagation, so that the model can reduce the error in subsequent predictions. The above training steps are repeated to perform multiple iterations of training on all data in the data training set. As the number of iterations increases, the model continuously adjusts the parameters and gradually learns the intrinsic relationship between historical fitting parameters and historical finished product quality data. Each iteration will yield a set of training results, which reflect the model's fit to the data and predictive ability at the current training stage.

[0026] Step S250: The multiple training results are validated using the data validation set to generate a validation token. Multiple validation training results are obtained based on the validation token. When the multiple validation training results converge, the adaptive control model is obtained. Specifically, after the model completes a certain number of iterations of training, the model is validated using the data validation set. The bonding-quality pairs in the data validation set are sequentially input into the trained model to obtain the model's prediction results for each bonding-quality pair. The model's prediction results are compared with the actual finished product quality data in the data validation set. A validation token is generated based on the comparison results. The validation token is a comprehensive evaluation indicator or a set of indicators for the model's performance. For example, it could be accuracy (the ratio of correctly predicted samples to the total number of samples), recall (the ratio of samples predicted as positive and actually positive to the actual number of positive samples), F1 score (an indicator combining accuracy and recall), or other specific performance indicators related to the bonding process, such as bonding accuracy error and finished product quality. Based on the generated verification tokens, multiple verification training results are obtained, reflecting the model's performance in the current verification stage. For example, if the verification token is accuracy, different accuracy values ​​correspond to the model's performance after different training rounds. The changing trends of multiple verification training results in the continuous verification process are observed. When the results tend to converge, that is, when the model's performance indicators on the data verification set no longer fluctuate significantly and the predetermined convergence criteria are met (such as accuracy reaching a certain threshold, error being less than a certain set value, etc.), it indicates that the model has learned a stable and reliable bonding-quality relationship pattern. The obtained model is the adaptive control model we need, which can be used to precisely control and optimize the bonding process of the conductive film of the new capacitive touch screen.

[0027] In one possible implementation, an adaptive control model is constructed. The multiple initial bonding parameters are synchronized to the adaptive control model, and bonding prediction is performed according to the bonding process parameters of the capacitive touchscreen conductive film to obtain multiple bonding control prediction information. Step S200 further includes step S260, where the multiple initial bonding parameters are synchronized to the adaptive control model through an input layer. Bonding analysis is performed according to the multiple bonding-quality pairs and the multiple initial bonding parameters according to the bonding process parameters to obtain multiple bonding information, including bonding deformation information and bonding quality information. Specifically, multiple initial bonding parameters (including bonding pressure value, bonding temperature setting, bonding speed, and other relevant equipment parameters, which are determined during the bonding equipment calibration stage based on the material fundamental parameters of the capacitive touchscreen conductive film) are used as input data to the adaptive control model and enter the model through the input layer. The input layer can standardize parameters of different types and magnitudes to ensure that the model correctly receives and processes them. The adaptive control model has learned the relationships implied by multiple bonding-quality pairs and mastered the bonding process parameter requirements during training. The bonding-quality pairs reflect the correlation between different bonding parameters and the final product quality in history, and the bonding process parameters define the ideal bonding condition range. Internally, the model performs bonding analysis based on the input bonding-quality pairs, initial bonding parameters, and bonding process parameters, simulating the bonding process. For example, based on the ideal pressure curve in the bonding process parameters, the initial bonding pressure parameters, and the bonding-quality pairs under similar historical pressure conditions, the model analyzes the impact of the current bonding pressure on the bonding quality. The analysis covers all aspects of the bonding process, ultimately obtaining multiple bonding information. Among these, the bonding deformation information is a quantitative description of the shape change during the bonding of the conductive film of the capacitive touchscreen, which is crucial for evaluating the bonding tightness and uniformity. The bonding quality information is a comprehensive evaluation of the bonding results, including the presence of air bubbles and whether the electrical performance after bonding meets standards, among other multi-dimensional quality indicators.

[0028] Step S270: Feedback the fitting deformation information to the data processing layer of the adaptive control model for control evaluation and generate multiple fitting scores. Specifically, the bonding deformation information is transmitted from the input of the model to the data processing layer. The data processing layer is one of the core components of the adaptive control model, responsible for processing and analyzing various feedback information. The information transmission channel is optimized to ensure the accuracy and timeliness of the data. In the data processing layer, the bonding deformation information is processed according to the preset control evaluation algorithm. The algorithm is based on a large amount of experimental data and theoretical models, and can accurately evaluate the impact of bonding deformation on bonding quality. For example, if the bonding deformation exceeds a certain threshold, it may mean that the bonding pressure is uneven or the temperature is too high, causing the material to soften excessively, thus affecting the bonding quality. Through control evaluation, multiple bonding scores are generated for each stage of the bonding process. The bonding score is a quantitative evaluation of the performance of different aspects in the bonding process. For example, different scores can be given for bonding in different areas, or corresponding scores can be given for different stages in the bonding process (such as the initial bonding and stable bonding stages). The scores can intuitively reflect the degree of deviation of the current bonding process from the ideal state.

[0029] Step S280: Based on the multiple bonding scores and the bonding quality information, bonding prediction is performed, and the multiple bonding control prediction information is output through the output layer. Specifically, bonding prediction is performed based on the generated multiple bonding scores and existing bonding quality information. The bonding scores reflect the dynamic situation of the current bonding process, while the bonding quality information provides an overall quality benchmark. Combining the two can more comprehensively consider various factors in the bonding process. For example, if the bonding score of a certain area is low, but the current bonding quality information shows that the overall electrical performance has not been significantly affected, the model can predict that the area may have problems in subsequent use and needs to be adjusted in time. Through the prediction algorithm in the model, the bonding scores and bonding quality information are comprehensively considered to generate multiple bonding control prediction information. The prediction information includes suggestions for adjusting various parameters in the future bonding process, such as whether it is necessary to increase or decrease them. The bonding control system adjusts bonding pressure, bonding temperature, and bonding speed, and predicts potential quality issues such as bubbles or decreased conductivity. This bonding control prediction information is output through an output layer, which converts the prediction information into a suitable format for interaction with an external bonding control system. The output information can be directly used to adjust the parameters of the bonding equipment in real time, thereby achieving precise control of the bonding process. During the bonding process, sensors installed on the equipment collect key information such as deformation and temperature distribution in real time and feed it back to the adaptive control model to form a closed-loop control. The closed-loop control mechanism can continuously correct the model's predictions and control strategies to ensure that the bonding process always moves towards high quality.

[0030] Step S300: Dynamically optimize the bonding of the conductive film of the capacitive touchscreen according to the multiple bonding control prediction information, and formulate a bonding control strategy. Specifically, the bonding dynamic optimization of the conductive film of the capacitive touchscreen according to the multiple bonding control prediction information and the formulation of a bonding control strategy involve: based on the prediction results of the adaptive control model, monitoring the bonding pressure in real time through a pressure sensor; if the pressure in a certain area is insufficient or too high, the control system sends a signal to the pressure regulating device to adjust it, ensuring uniform bonding between the conductive film and the touchscreen substrate; monitoring the temperature in real time using a temperature sensor in the bonding area; and adaptively adjusting the heater power through the control system based on temperature changes and prediction information to maintain temperature stability, as temperature affects material properties and bonding quality; simultaneously, comprehensively considering the bonding pressure and temperature changes, adjusting the bonding speed in real time; appropriately increasing the speed to improve efficiency when the pressure and temperature are suitable, and reducing the speed to maintain quality when both are abnormal. Integrating the pressure, temperature, and speed adjustment measures into a highly dynamic and adaptable bonding control strategy ensures that the bonding process is in optimal condition, achieving high-quality bonding and efficient production.

[0031] Step S400: Execute the bonding control strategy to position and bond the conductive film of the capacitive touchscreen, generate bonded product information, and perform quality inspection on the bonded product information to obtain finished product quality inspection data. Specifically, the bonding control strategy is executed to position and bond the conductive film of the capacitive touchscreen. First, the capacitive touchscreen conductive film and the touchscreen substrate are accurately placed at the designated position on the bonding equipment, and the equipment completes parameter initialization according to the strategy. The relative position of the two is determined by a high-precision positioning system using optical, mechanical, or a combination of both, so that the positional error is controlled within the micrometer level. During bonding, the two are initially brought into contact with low pressure and moderate speed according to the strategy. During the process, the pressure is increased and the temperature is stabilized according to the strategy until the bonding of the entire bonding area is completed, generating bonded product information including bonding parameter records, time information, positioning accuracy data, and equipment operating status. The appearance is checked for scratches, impurities, bubbles, and whether the bonding edges are neat and warped using visual inspection or high-resolution optical inspection equipment. The conductivity of the conductive film, the capacitance value and distribution of the touchscreen, and the electrical insulation performance are measured using professional electrical testing equipment. Physical properties are tested by measuring thickness and performing tensile or peel tests. The data on appearance, electrical properties, and physical performance are collected and summarized, and then classified and generated into finished product quality assessment indicators such as conductivity value, number of air bubbles, whether the appearance is qualified, and whether the bonding is firm, so as to obtain finished product quality inspection data.

[0032] In one possible implementation, the bonding control strategy is executed to position and bond the conductive film of the capacitive touch screen, generating bonding product information, and the bonding product information is subjected to quality inspection to obtain finished product quality inspection data. Step S400 further includes step S410, performing planar analysis based on the capacitive touch screen conductive film to construct a bonding space coordinate system. Specifically, the planar characteristics of the conductive film of the capacitive touchscreen are analyzed, and its length, width, and other dimensions are precisely measured to the micrometer level, as this dimensional information is crucial for subsequent positioning. Simultaneously, a high-precision flatness measuring instrument is used to analyze the surface flatness of the conductive film, detecting any minute undulations or distortions, as these can affect the bonding quality. The geometric center of the conductive film must also be determined, serving as an important reference point for constructing a coordinate system. A two-dimensional planar coordinate system is established based on the length and width directions of the conductive film, with this geometric center as the origin. If thickness variations during bonding or the operation of the bonding equipment in three-dimensional space need to be considered, a three-dimensional coordinate system is established, with the direction perpendicular to the conductive film plane designated as the third dimension. When establishing the coordinate system, it is essential to ensure that the coordinate axis directions match the movement and measurement directions of the bonding equipment to facilitate subsequent coordinate calculations and alignment operations. The coordinate axis unit is determined according to the measurement accuracy, typically at the micrometer level to ensure coordinate accuracy.

[0033] Step S420: The conductive film and capacitive touchscreen are synchronized to the bonding space coordinate system for alignment, generating multiple bonding positioning coordinates. Specifically, the capacitive touchscreen and conductive film are incorporated into the pre-constructed bonding space coordinate system. A positioning device is used to fix them at their respective positions on the bonding device. Then, sensors convert their position information into coordinate values ​​in the coordinate system, ensuring their initial positions are accurate, as even small deviations can lead to inaccurate bonding. Next, a high-precision alignment system based on optical principles (which can be achieved by emitting a laser beam or using an optical camera) is used to align the capacitive touchscreen and conductive film. This system adjusts their positions by identifying the positions of tiny markers set on their edges or at specific locations in the coordinate system, making their coordinates as close as possible to the ideal alignment state. During the alignment process, continuous measurement and adjustment are required until the preset alignment accuracy requirement is achieved. Once the capacitive touchscreen and conductive film are aligned, multiple bonding positioning coordinates are generated based on their positions in the bonding space coordinate system. These coordinates not only cover the center coordinates of both, but also the coordinates of the edges and key areas. The number and distribution of coordinate points are determined according to the bonding accuracy requirements and the shape and size of the conductive film and capacitive touchscreen. Usually, when the shape is complex, the size is large, or the bonding accuracy requirements are high, more positioning coordinate points are needed.

[0034] Step S430: Positioning calibration is performed according to the multiple bonding positioning coordinates to determine the bonding positioning data. Specifically, based on the generated multiple bonding positioning coordinates, the positions of the capacitive touchscreen and the conductive film are precisely calibrated. A micro-displacement adjustment device is used, which can adjust the positions of the capacitive touchscreen and the conductive film at a scale of micrometers or even smaller. For each bonding positioning coordinate point, its actual coordinates are compared with the ideal alignment coordinates. If there is a deviation, the micro-displacement adjustment device is used to make fine adjustments in the corresponding direction. For example, if a coordinate point deviates in the x-axis direction, it is adjusted by the micro-displacement adjustment mechanism in the x-axis direction to bring the coordinate point back to the ideal position. After completing the calibration of all bonding positioning coordinate points, the final bonding positioning data is determined. The data includes the accurate coordinate values ​​of each calibrated coordinate point, the adjustment amount information during the calibration process, and the overall position deviation information after calibration. The bonding positioning data comprehensively reflects the final position state of the capacitive touchscreen and the conductive film before bonding, providing a precise positioning basis for subsequent bonding operations.

[0035] Step S440: Based on the bonding positioning data, the bonding equipment is activated to execute the bonding control strategy to bond the conductive film of the capacitive touchscreen, generating the bonded product information. Specifically, the bonding positioning data is input into the control system of the bonding equipment. The control system then activates various execution components, such as activating the pressure control system to prepare to apply bonding pressure according to a preset pressure curve, activating the temperature control system to adjust the temperature of the bonding area to a suitable initial temperature, and activating the movement control system to prepare to perform the bonding operation according to a predetermined trajectory and speed. The bonding equipment bonds the conductive film of the capacitive touchscreen according to the bonding control strategy. During the process, the pressure control system applies corresponding pressure at different stages according to the pressure parameters in the strategy to ensure a tight bond between the conductive film and the capacitive touchscreen. The temperature control system maintains a stable temperature in real time to ensure that the material properties are not affected by temperature changes. The movement control system moves both components according to a predetermined trajectory and speed, gradually completing the bonding process throughout the bonding area. Simultaneously, the bonding process is dynamically adjusted based on real-time feedback information such as pressure changes and temperature fluctuations to ensure quality. After bonding is completed, information about the bonded product is generated, including records of parameters such as pressure, temperature, movement speed and position at various moments during the bonding process, as well as finished product quality information such as whether there are air bubbles, flatness, and bonding strength.

[0036] In one possible implementation, the bonding control strategy is executed to position and bond the conductive film of the capacitive touch screen, generating bonded product information. The bonded product information is then subjected to quality inspection to obtain finished product quality inspection data. Step S400 further includes step S450, which involves performing feature analysis on the capacitive touch screen conductive film based on the historical bonding data log to generate multiple feature parameters, and formulating multiple quality inspection standards based on the multiple feature parameters. Specifically, this study analyzes historical bonding data logs containing extensive data on the bonding process and finished products of conductive films used in capacitive touchscreens. From a bonding process perspective, it examines the variations in parameters such as pressure, temperature, and bonding time during bonding of different batches of conductive films. From a material properties perspective, it investigates the impact of conductive film composition, thickness, and hardness on bonding quality. Statistical analysis methods are used to calculate the mean, variance, maximum, and minimum values ​​of each parameter to identify distribution patterns. For example, analyzing the mean and variance of conductive film resistance across different batches helps understand its stability. Correlation analysis is employed to determine the relationships between different parameters, such as the correlation between bonding pressure and the conductivity of the finished product. Based on this, multiple characteristic parameters are generated, covering physical characteristics such as the average thickness, hardness, and surface roughness of the conductive film. Electrical characteristic parameters, such as the average resistance, capacitance, and rate of change of the conductive film in different areas, and process characteristic parameters, such as the average pressure, temperature range, and bonding time during the bonding process, comprehensively describe the key characteristics of the production and use of the conductive film for capacitive touch screens. Based on the generated multiple characteristic parameters, quality inspection standards are formulated. For physical characteristic parameters, the acceptable thickness range is determined based on the functional requirements of the conductive film and the thickness range of historical high-quality products. For electrical characteristic parameters, upper and lower limits of resistance values ​​are set based on the performance requirements of the touch screen and the resistance value range of products with good conductivity in the past. For process characteristic parameters, pressure, temperature, and time standards are formulated during the bonding process based on the bonding parameter range of historically stable high-quality products, thus providing a clear basis for subsequent product quality inspection.

[0037] Step S460: Based on the bonded product information, the capacitive touchscreen conductive film is tested and analyzed according to the multiple quality inspection standards to determine multiple test items. Specifically, the bonded product information covers various data after the capacitive touchscreen conductive film produced this time has been bonded, including bonding parameter records and finished product appearance. Based on this information and the established quality inspection standards, the capacitive touchscreen conductive film is comprehensively tested and analyzed. From the perspective of conductivity performance, conductivity performance test items are determined to check whether the conductive film can meet the conductivity requirements of the touchscreen, ensuring that the current is evenly distributed on the conductive film to achieve accurate touch function. For the light transmittance test item, considering the touchscreen display effect, professional light transmittance testing equipment is used to measure the light transmittance of the conductive film to ensure that it does not affect the display. In the durability test item, environmental conditions and operating conditions such as frequent touch, temperature changes, and humidity changes during long-term use of the touchscreen are simulated to evaluate the performance stability of the conductive film. Combined with other contents in the quality inspection standards, specific test items such as resistance uniformity, bonding integrity, adhesion test, and appearance quality are determined to comprehensively evaluate the product quality.

[0038] Step S470: Perform quality tests on the conductive film of the capacitive touchscreen according to the multiple test items, and generate multiple quality inspection reports. These multiple quality inspection reports correspond to the multiple test items. Specifically, for each test item, corresponding testing equipment and methods are used to perform quality tests on the conductive film of the capacitive touchscreen. In conductivity testing, high-precision resistance measuring instruments are used to measure resistance values ​​at multiple points on the conductive film surface to evaluate its uniformity and stability. For transmittance testing, the conductive film is placed in a transmittance testing device to measure transmittance values ​​under different wavelengths of light. Durability testing involves placing the conductive film on a test platform simulating actual usage environments and detecting performance changes after a certain number of simulated touch, temperature, and humidity cycles. Resistance uniformity testing uses professional resistance scanning equipment to record the resistance distribution in different areas of the conductive film surface to assess uniformity. Adhesion integrity testing uses optical or ultrasonic testing instruments to detect gaps, bubbles, and other defects in the bonding area to ensure quality. Adhesion testing equipment quantitatively assesses the bonding strength between the conductive film and the substrate to ensure it does not detach during use. Appearance quality inspection involves visual inspection combined with high-resolution microscopes to carefully examine the conductive film surface for scratches, impurities, or other defects. Based on the results of each test item, corresponding quality inspection reports are generated. For example, the resistance uniformity report records in detail the resistance value, average value, standard deviation, and variation trend of each test point on the surface of the conductive film. The bonding integrity report includes the number, size, and location of gaps and bubbles in the bonding area. The adhesion test report gives the bonding force value. The appearance quality report describes the length, depth, number, and type and distribution of scratches and impurities. Each quality inspection report is closely related to a specific test item and comprehensively reflects the quality status of the conductive film of the capacitive touch screen in this aspect.

[0039] Step S480: Based on the multiple quality inspection reports, perform a finished product impact analysis on the capacitive touchscreen conductive film to obtain multiple impact coefficients. Specifically, comprehensively analyze the multiple quality inspection reports to assess the degree of impact of the quality problems reflected in each inspection item on the finished performance of the capacitive touchscreen conductive film. For the resistance uniformity report, analyze the impact of uneven resistance values ​​on the touchscreen's touch accuracy and response speed. If the resistance value in some areas is too high or too low, it may cause inaccurate touch or local touch failure, affecting the performance of the finished product. Regarding the bonding integrity, analyze the impact of gaps or bubbles on the touchscreen's display effect and durability, considering their impact on light refraction and reflection, as well as the delamination problems that may occur during long-term use, to determine the degree of impact on the finished product's quality. For the adhesion test results, assess the risk of detachment caused by insufficient bonding strength between the conductive film and the substrate during use and the degree of damage to the touchscreen's function. For appearance quality issues, consider the impact of scratches and impurities on the touchscreen's visual effect, touch sensitivity, and long-term stability. By establishing mathematical models or using empirical evaluation methods, an impact coefficient is determined for each quality inspection item. This coefficient represents the weight of the quality problem's impact on the finished product's quality. For example, conductivity, as a key performance indicator, may have a higher impact coefficient because it directly determines whether the touchscreen can work properly. Meanwhile, relatively minor appearance defects that do not affect the touchscreen's functionality and long-term stability may have a lower impact coefficient. These impact coefficients accurately quantify the degree of impact of each quality problem on the finished product.

[0040] Step S490: The multiple quality inspection reports are correlated and integrated according to the multiple influence coefficients to obtain the finished product quality inspection data. Specifically, based on the obtained multiple influence coefficients, the multiple quality inspection reports are correlated and integrated. The data in each quality inspection report is multiplied by its corresponding influence coefficient, so that the data of different test items are weighted after considering their degree of influence on the quality of the finished product. For example, for the resistance value data in the resistance uniformity report, multiplying it by its influence coefficient highlights the importance of resistance uniformity to the quality of the finished product. Through correlation and integration, all weighted quality inspection data are summarized together to form a comprehensive and integrated set of finished product quality inspection data. The set not only includes the original data of each quality inspection item, but also considers their different degrees of influence on the quality of the finished product. It can accurately and objectively reflect the overall quality status of the conductive film of the capacitive touch screen, providing a strong basis for product quality assessment, improvement and decision-making.

[0041] Step S500: The finished product quality inspection data is fed back to the adaptive control model for response, generating an adaptive control optimization model. This model intelligently optimizes and updates the bonding process parameters of the conductive film on the capacitive touchscreen. Specifically, a dedicated data feedback channel is established in the bonding process system from the finished product quality inspection module to the adaptive control model to ensure accurate and timely data transmission, adhering to a specific communication protocol. Simultaneously, the finished product quality inspection data undergoes format matching and preprocessing, including format conversion, noise removal, and outlier handling, ensuring reliable data quality. After receiving the data, the adaptive control model comprehensively analyzes it, classifying and parsing it according to different quality indicators. It then initiates a parameter adjustment mechanism, traces bonding process parameters related to quality issues, determines their potential impact based on built-in algorithms, and subsequently adjusts the model structure or parameters, incorporating new algorithms and rules to generate the adaptive control optimization model. The optimization model calculates and optimizes bonding process parameters (such as pressure, temperature, speed, and time) based on finished product quality inspection data and its own algorithm. The calculated updated values ​​are accurately and timely transmitted to the bonding equipment control system. The control system adjusts the bonding process accordingly to achieve intelligent optimization and updates. Subsequent bonding process data is then fed back to the model, forming a closed-loop control for continuous improvement.

[0042] In one possible implementation, the finished product quality inspection data is fed back to the adaptive control model for response, generating an adaptive control optimization model. The adaptive control optimization model intelligently optimizes and updates the bonding process parameters of the capacitive touchscreen conductive film. Step S500 further includes step S510, where the finished product quality inspection data is fed back to the adaptive control model to extract the bonding parameters of the capacitive touchscreen conductive film. Specifically, a reliable data transmission link is established from the finished product quality inspection stage to the adaptive control model to ensure the integrity and consistency of data transmission and avoid data loss or damage. The finished product quality inspection data, encompassing multiple aspects such as electrical, physical, and appearance quality after the capacitive touchscreen conductive film bonding is completed, is accurately fed back to the adaptive control model. This model has a dedicated data receiving module for identifying and receiving this data. After receiving the data, the adaptive control model starts the parameter extraction program. Through specific internal algorithms and data processing logic, it extracts parameters related to the bonding of the conductive film of the capacitive touch screen from a large amount of quality inspection data. These parameters include pressure values ​​such as pressure distribution in different areas during bonding, temperature change curves and temperature conditions such as temperature settings at different stages of the bonding process, speed parameters such as speed adjustment and movement speed of the bonding equipment in different areas, and physical parameters related to the bonding equipment such as vacuum degree and bonding time. This provides valuable information for analyzing the bonding process.

[0043] Step S520: A correlation analysis is performed on the finished product quality inspection data and the bonding parameters to generate multiple correlation coefficients. Specifically, data is prepared for the correlation analysis in the adaptive control model. The finished product quality inspection data and the extracted bonding parameters are organized and stored in a form that supports efficient data access and calculation, such as arrays, matrices, or data tables. Then, the correlation analysis algorithm is started to comprehensively traverse and analyze the finished product quality inspection data and bonding parameters. The correlation between each quality inspection indicator (such as the conductivity of the finished product) and each bonding parameter (such as pressure, temperature, speed, etc.) is calculated. Mathematical methods such as Pearson correlation coefficient calculation and Spearman's rank correlation coefficient calculation can be used to obtain the correlation value reflecting the strength of the linear or nonlinear relationship between each pair of data and parameters. Finally, multiple correlation coefficients are generated based on the analysis results. Their values ​​range from -1 to 1, with an absolute value close to 1 indicating a strong correlation and close to 0 indicating a weak correlation. This clearly shows the impact of each bonding parameter on the finished product quality.

[0044] Step S530: Optimize the bonding process parameters according to the multiple correlation coefficients to generate optimized bonding process parameters. Specifically, determine the optimization target of the bonding process parameters based on the generated multiple correlation coefficients. When a bonding parameter is strongly correlated with a key finished product quality indicator (the absolute value of the correlation coefficient is close to 1), the goal is to adjust the bonding parameter to improve the quality of the relevant finished product. For example, if the bonding temperature is strongly correlated with the electrical performance indicators of the finished product and the current electrical performance is problematic, the optimization target is to adjust the bonding temperature parameter to improve the electrical performance. Based on the optimization target, a suitable optimization algorithm (such as gradient descent-based algorithm, genetic algorithm, simulated annealing algorithm, etc.) is used to optimize the bonding process parameters. Taking the gradient descent algorithm to optimize the bonding pressure parameter as an example, the algorithm calculates the gradient direction based on the relationship between the pressure parameter and the finished product quality indicated by the correlation coefficient, and adjusts the pressure parameter in the direction of improving the finished product quality. During the adjustment, the influence of other related parameters and process constraints (such as the adjustable range of pressure, the impact on equipment, etc.) are considered to ensure that the optimized parameters are feasible in actual operation. After adjusting the bonding process parameters through optimization algorithms, optimized bonding process parameters are generated. These optimized parameters are improvements on the original parameters, taking into account the correlation between finished product quality inspection data and bonding parameters, which can more effectively improve the quality of finished products. For example, the optimized bonding pressure parameter may increase or decrease in some areas, the bonding temperature curve may be more finely adjusted, and the bonding speed may have new set values ​​at different stages. These optimized parameters can provide specific guidance for subsequent bonding process improvements.

[0045] Step S540: The bonding process optimization parameters are fused with the multiple historical bonding parameters. Based on the fusion result, the multiple historical finished product quality data are updated, generating multiple bonding-quality optimization pairs. Specifically, the generated bonding process optimization parameters are fused with multiple historical bonding parameters. This fusion is not a simple addition or replacement, but a comprehensive processing based on a certain strategy. For example, different weights are assigned based on the reliability and effectiveness of historical bonding parameters, and then a weighted average is taken with the optimization parameters. Parameters that are stable and effective in historical bonding are given higher weights, while new optimization parameters are given appropriate weights based on their rationality and ability to address current quality issues. This weighted fusion method combines new optimization information with historical experience to form a new set of bonding parameters. The multiple historical finished product quality data are updated based on the fused bonding parameters. Because the new parameters will affect the finished product quality, existing quality assessment models and methods are used in conjunction with the fused parameters to simulate or predict the historical finished product quality data. For example, when the pressure parameters change after fusion, the data of finished product quality indicators such as conductivity and bonding firmness under the new pressure conditions are recalculated based on the pressure-effect model for finished product quality, resulting in updated historical finished product quality data. Based on the updated historical finished product quality data and the fused bonding parameters, multiple bonding-quality optimization pairs are generated. Each optimization pair consists of a set of fused bonding parameters and corresponding updated finished product quality data. These optimization pairs reflect the expected finished product quality under the new parameter settings and more accurately reflect the current state after process improvement than the original bonding-quality pairs, providing a more valuable data foundation for further optimization of the model and process.

[0046] Step S550: Update the data training set according to the multiple bonding-quality optimization pairs to obtain an optimized data training set for correcting the adaptive control model, generating the adaptive control optimization model. Specifically, update the data training set using the newly generated multiple bonding-quality optimization pairs, add these optimization pairs to the original training set, and adjust any parts that conflict with or are redundant with the new data. If the new bonding-quality optimization pairs differ significantly from the data in the original training set under the same conditions, further analysis is needed to determine the cause. This may involve deleting inaccurate data from the original training set or verifying and correcting the new data. This allows the data training set to more comprehensively and accurately reflect the relationship between bonding process parameters and finished product quality, improving the quality of the training set. The updated optimized data training set contains richer and more accurate bonding-quality information, with a more reasonable data distribution, covering more process conditions and quality results. This provides a more sufficient sample space for model learning, helping to improve the model's generalization ability and adaptability to different process conditions. The adaptive control model is corrected by optimizing the training set with data. During training, the bonding-quality optimization pairs in the optimized training set are used as input data. By adjusting the model parameters (such as the weights and biases of the neural network model, the membership function parameters of the fuzzy logic model, etc.), the model can better fit the new data. During training, various training algorithms and optimization methods such as backpropagation algorithm and stochastic gradient descent algorithm can be used to continuously adjust the parameters until the model achieves optimal performance on the optimized training set. The corrected model is the adaptive control optimization model, which can more accurately predict the quality of the finished product based on the bonding process parameters, and provide more precise control and optimization for the bonding process of conductive film in capacitive touch screens.

[0047] In one possible implementation, the bonding process parameters are optimized according to the multiple correlation coefficients to generate optimized bonding process parameters. Step S530 further includes step S531, determining an optimization objective function based on the multiple correlation coefficients. Specifically, the previously calculated multiple correlation coefficients are analyzed. The correlation coefficients reflect the degree of correlation between the finished product quality inspection data and the bonding parameters. By observing the magnitude and sign of each correlation coefficient, the bonding parameters that have a significant impact on the finished product quality and the relationship between quality indicators are determined. For example, if the absolute value of the correlation coefficient between bonding pressure and the bonding uniformity index of the finished product is found to be large, it indicates that the bonding pressure has a significant impact on the bonding uniformity. Based on the analysis results of the correlation coefficients, the optimization objective function is determined. Since the goal is to improve the bonding quality, the optimization objective function is set to minimize indicators closely related to the bonding quality, such as bonding uniformity error or adhesion deviation. For example, when the uniformity of bonding is crucial to the quality of the finished product, the objective function can be defined as minimizing the bonding uniformity error. That is, by adjusting the bonding process parameters, the conductive film can be bonded more evenly on the touch screen, reducing quality problems caused by uneven bonding, such as poor local conductivity or the appearance of bubbles.

[0048] Step S532: Introduce parameter constraints. Randomly select the bonding process parameters according to the parameter constraints to generate an initial parameter population. Specifically, the parameter constraints are set based on the physical limitations of the actual bonding process and the performance of the equipment. For example, upper and lower limits are set for bonding pressure, because excessive bonding pressure may damage the conductive film or touch screen substrate, while insufficient pressure cannot guarantee a good bonding effect. Similarly, upper and lower limits are also set for bonding speed, temperature, and electrostatic control strength. Too fast a bonding speed may lead to inaccurate bonding, too high or too low a temperature may affect the material performance, and improper electrostatic control strength may attract impurities and affect the bonding quality. The parameter constraints ensure that the generated bonding process parameters are feasible in actual operation. Within the range of satisfying the parameter constraints, the bonding process parameters are randomly selected. For example, multiple pressure values ​​are randomly generated within the upper and lower limits of the bonding pressure. Corresponding values ​​are also randomly selected within the set range of speed, temperature and electrostatic control intensity. The randomly selected bonding process parameters are combined to form multiple parameter combinations. Each parameter combination is an individual. Many such individuals constitute the initial parameter population. The initial parameter population provides the initial search space for the subsequent optimization process.

[0049] Step S533: Evaluate the initial parameter population according to the optimization objective function to generate multiple fitness values. Specifically, each parameter combination in the initial parameter population is substituted into the optimization objective function. For each parameter combination, its behavior during the bonding process is simulated. Based on the optimization objective function (e.g., minimizing bonding uniformity error or adhesion deviation), the bonding quality result produced under that parameter combination is calculated. Existing bonding process models and quality evaluation methods are used, combined with the given parameter combinations, to predict the magnitude of bonding uniformity error or adhesion deviation. Based on the calculation results of the optimization objective function, a fitness value is generated for each parameter combination. The fitness value represents the degree to which the parameter combination achieves the optimization objective (e.g., reducing bonding uniformity error or adhesion deviation). For example, if a parameter combination can make the bonding uniformity error very small, then its fitness value is high; conversely, if a parameter combination leads to a large bonding uniformity error, its fitness value is low. In this way, each individual in the initial parameter population is assigned a fitness value, which will be used for subsequent selection and cross-validation operations.

[0050] Step S534: Based on the multiple fitness values, the initial parameter population is cross-validated to generate multiple bonding process parameter sets. Each bonding process parameter in the multiple bonding process parameter sets has a bonding optimization effect. Specifically, according to the generated multiple fitness values, a selection operation is performed on the initial parameter population, selecting individuals with higher fitness values ​​because the parameter combinations corresponding to these individuals perform better in achieving the optimization goal. For example, individuals corresponding to parameter combinations that minimize bonding uniformity error or adhesion deviation are selected. The selection operation simulates the process of natural selection, giving excellent individuals more opportunities to participate in the subsequent reproduction (cross-validation) process. The selected individuals are then cross-validated. Cross-validation is the process of generating new parameter combinations by exchanging some parameter values. For example, for two selected parameter combinations (individuals), one or more parameter positions can be randomly selected, and then the parameter values ​​of the two individuals at these positions can be exchanged to generate new parameter combinations. Through multiple such cross-validation operations, multiple new bonding process parameter sets are generated. The newly generated parameter sets inherit some characteristics of the excellent individuals and generate new possibilities through cross-validation. Each parameter set has a certain bonding optimization effect, that is, it can reduce bonding uniformity error or adhesion deviation to a certain extent.

[0051] Step S535: Based on multiple bonding optimization effects, the multiple bonding process parameter groups are screened and recombined to generate the bonding process optimization parameters. Specifically, the generated multiple bonding process parameter groups are screened based on the bonding optimization effect of each parameter group, that is, their actual performance in reducing bonding uniformity error or adhesion deviation. For example, each parameter group is re-substituted into the optimization objective function for calculation, and their optimization effects are compared. Parameter groups that can significantly reduce bonding uniformity error or adhesion deviation are selected, while parameter groups with insignificant effects or that may even worsen quality problems are eliminated. The screened parameter groups are recombined. During the recombination process, the parameter values ​​in the parameter groups can be fine-tuned to further optimize the parameter combination. For example, if it is found that the bonding pressure in a certain parameter group can achieve a good optimization effect within a certain range, but there is still room for improvement, the pressure value can be adjusted more finely within that range. Through screening and recombination operations, the final bonding process optimization parameters are generated. The parameters can optimize the bonding process to the greatest extent and improve the bonding quality while meeting the parameter constraints.

[0052] This application employs material parameters based on the conductive film of a capacitive touchscreen to calibrate the bonding equipment and generate initial bonding parameters. An adaptive control model is used for bonding prediction and dynamic optimization, and a bonding control strategy is formulated and executed to achieve precise bonding of the conductive film. Finished product data is obtained through quality inspection, and the inspection results are fed back to the model to optimize the bonding parameters. This enables intelligent dynamic adjustment and continuous improvement of the bonding process, achieving the technical effect of improving the bonding uniformity and waterproof / dustproof performance of the capacitive touchscreen conductive film.

[0053] Based on the foregoing embodiments, this application also provides an electronic device and a computer-readable storage medium storing a computer program. When the computer program is executed by the processor of the electronic device, it can implement the methods described in any of the preceding embodiments.

[0054] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention. This electronic device is in the form of a general-purpose computing device, and its components may include, but are not limited to, an input device 301, a processor 302, a memory 303, and an output device 304. The processor 302 may be one or more; the memory 303 may include a computer-readable medium and at least one program product having a set (at least one) of program modules configured to perform the functions of the embodiments of this application.

[0055] The memory 303 shown in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable storage medium can be, but is not limited to, infrared, semiconductor systems, devices or components, or any combination thereof, used to store software programs, computer-executable programs and modules, such as the program instructions / modules corresponding to the capacitive touch screen conductive film bonding process optimization method in this embodiment of the invention. The processor 302 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 303, thereby realizing the above-mentioned capacitive touch screen conductive film bonding process optimization method.

[0056] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for optimizing the lamination process of a conductive film for a capacitive touch screen, characterized in that, The method comprises: Calibrating the fitting device based on the material basic parameters of the conductive film of the capacitive touch screen to generate a plurality of initial fitting parameters; Building an adaptive control model, synchronizing the plurality of initial fitting parameters to the adaptive control model, and predicting the fitting process of the conductive film of the capacitive touch screen according to the fitting process parameters to obtain a plurality of fitting control prediction information; According to the plurality of fitting control prediction information, the conductive film of the capacitive touch screen is dynamically optimized, and a fitting control strategy is formulated; Performing the fitting control strategy to position and fit the conductive film of the capacitive touch screen to generate fitting finished product information, and performing quality detection on the fitting finished product information to obtain finished product quality detection data; The finished product quality detection data is fed back to the adaptive control model for response to generate an adaptive control optimization model, and the fitting process parameters of the conductive film of the capacitive touch screen are intelligently optimized and updated through the adaptive control optimization model; The finished product quality detection data is fed back to the adaptive control model to extract the fitting parameters of the conductive film of the capacitive touch screen; Correlation analysis is performed on the finished product quality detection data and the fitting parameters to generate a plurality of correlation coefficients; According to the plurality of correlation coefficients, the fitting process parameters are optimized to generate fitting process optimization parameters; The fitting process optimization parameters are fused with a plurality of historical fitting parameters, and a plurality of historical finished product quality data are updated according to the fusion result to generate a plurality of fitting-quality optimization pairs; According to the plurality of fitting-quality optimization pairs, the data training set is updated to obtain a data optimization training set, the adaptive control model is corrected, and the adaptive control optimization model is generated; According to the plurality of correlation coefficients, the fitting process parameters are optimized to generate fitting process optimization parameters, the method comprising: Based on the plurality of correlation coefficients, an optimization objective function is determined; Parameter constraint conditions are introduced, the fitting process parameters are randomly selected according to the parameter constraint conditions to generate an initial parameter population; According to the optimization objective function, the initial parameter population is evaluated to generate a plurality of fitness values; Based on the plurality of fitness values, the initial parameter population is cross-validated to generate a plurality of fitting process parameter groups, and each fitting process parameter of the plurality of fitting process parameter groups has a fitting optimization effect; According to a plurality of fitting optimization effects, the plurality of fitting process parameter groups are screened and recombined to generate the fitting process optimization parameters. The adaptive control model is built, the method comprising:

2. The method of claim 1, wherein the method further comprises: Accessing historical fitting data logs, extracting a plurality of historical fitting parameters and a plurality of historical finished product quality data based on the historical fitting data logs, and the plurality of historical fitting parameters and the plurality of historical finished product quality data have a corresponding relationship; Adaptive precision control algorithm is used to analyze the plurality of historical fitting parameters and the plurality of historical finished product quality data to determine a plurality of fitting-quality pairs; ​ According to the multiple fitting-quality pairs, determine a data training set, a data supervision set, and a data verification set; According to the data supervision set, perform supervised iterative control training on the data training set to obtain multiple training results; According to the data verification set, perform fitting verification on the multiple training results to generate a verification token, and according to the verification token, obtain multiple verification training results. When the multiple verification training results tend to converge, the adaptive control model is obtained.

3. The method of claim 2, wherein the method further comprises: Synchronize the multiple initial fitting parameters to the adaptive control model to perform fitting prediction according to the fitting process parameters of the conductive film of the capacitive touch screen, and obtain multiple fitting control prediction information. The method comprises: Synchronize the multiple initial fitting parameters to the adaptive control model through an input layer, perform fitting analysis on the multiple fitting-quality pairs and the multiple initial fitting parameters according to the fitting process parameters, and obtain multiple fitting information. The multiple fitting information includes fitting deformation information and fitting quality information; Feed back the fitting deformation information to the data processing layer of the adaptive control model for control evaluation, and generate multiple fitting scores; Based on the multiple fitting scores and the fitting quality information, perform fitting prediction to obtain the multiple fitting control prediction information, and perform data output through an output layer.

4. The method of claim 1, wherein the method further comprises: determining a thickness of the conductive material; and determining a thickness of the dielectric material. Perform the fitting control strategy on the capacitive touch screen conductive film to generate fitting finished product information. The method comprises: Based on the capacitive touch screen conductive film, perform plane analysis to construct a fitting space coordinate system; Synchronize the conductive film and the capacitive touch screen to the fitting space coordinate system for alignment to generate multiple fitting positioning coordinates; According to the multiple fitting positioning coordinates, perform positioning calibration to determine fitting positioning data; According to the fitting positioning data, start the fitting equipment to perform the fitting control strategy on the capacitive touch screen conductive film to generate the fitting finished product information.

5. The method of claim 2, wherein the method further comprises: determining a thickness of the conductive layer; and determining a thickness of the dielectric layer. Perform quality detection on the fitting finished product information to obtain finished product quality detection data. The method comprises: According to the historical fitting data record log, perform feature analysis on the capacitive touch screen conductive film to generate multiple feature parameters, and based on the multiple feature parameters, formulate multiple quality detection standards; Based on the fitting finished product information, perform test analysis on the capacitive touch screen conductive film according to the multiple quality detection standards to determine multiple test items; According to the multiple test items, perform quality testing on the capacitive touch screen conductive film to generate multiple quality detection reports. The multiple quality detection reports and the multiple test items have a corresponding relationship; Based on the multiple quality detection reports, perform finished product influence analysis on the capacitive touch screen conductive film to obtain multiple influence coefficients; According to the multiple quality detection reports, associate and integrate the multiple influence coefficients to obtain the finished product quality detection data.

6. An electronic device, comprising: The electronic device comprises: A memory for storing executable instructions; A processor for executing the executable instructions stored in the memory to implement the capacitive touch screen conductive film fitting process optimization method of any one of claims 1 to 5.

7. A computer readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by the processor, implements the method for optimizing the process of attaching the conductive film of the capacitive touch screen as claimed in any one of claims 1-5.

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