A method for manufacturing a packaging substrate for an LED electronic display screen

Abstract

The present invention discloses a method for manufacturing a packaging substrate for an LED electronic display screen, and relates to the technical field of circuit board processing for LEDs. The present invention integrates a material requirement planning (MRP) system with CNC cutting equipment and utilizes an intelligent typesetting algorithm based on a genetic algorithm to maximize the utilization of substrate materials and reduce waste. Secondly, in the intelligent drilling stage, an intelligent drilling adjustment system constructed by a deep Q learning algorithm is adopted to achieve real-time adjustment and optimization of drilling parameters, thereby ensuring hole position accuracy and hole shape consistency. In the intelligent plating process, a neural network model based on deep learning is utilized to monitor and adjust electroplating parameters in real time, thereby improving the uniformity and adhesion of the electroplating layer. Finally, in the pressing and browning and resin grinding stages, an automatic plate grinding machine is used for resin grinding to ensure the flatness of the substrate surface.

Description

Technical Field

[0001] The present invention relates to the technical field of LED circuit board processing, in particular to a method for manufacturing a packaging substrate of an LED electronic display screen. Background Art

[0002] The packaging process of LED electronic display screens is to protect LED chips and circuit components, provide good heat dissipation performance, and facilitate installation and maintenance; common LED display screen packaging forms include SMD packaging, lamp bead packaging, packaging module, and COB packaging. In SMD packaging, the LED chip is directly mounted on the PCB, connected to the circuit by welding, and then encapsulated with transparent epoxy resin. It has the advantages of small size, light weight, and easy installation. In lamp bead packaging, the LED chip is installed in a small plastic or metal shell, usually with two pins. This packaging method is often used in outdoor LED displays. Due to its relatively strong structure, it can better withstand harsh environmental conditions. The packaging module is a larger and more complete packaging form, including LED chips, drive circuits, power supplies and other components to form a fully functional LED display module. The packaging module is usually installed on a bracket or frame to build the entire LED display screen. In COB packaging, multiple LED chips are directly mounted on the PCB and connected by conductive glue. COB packaging has the characteristics of high density and high brightness.

[0003] For example, patent publication number CN111263518B discloses a method for manufacturing a package substrate for an LED electronic display screen, comprising the following steps: cutting → drilling → VCP electroplating → pressing and browning → solder mask plugging → baking and curing → resin polishing → outer dry film → etching → outer AOI inspection → solder mask → solder mask curing → sandblasting → nickel, silver, and gold electroplating → CNC molding → finished product cleaning → final inspection → reliability → packaging. This method ensures a drilling accuracy of ±0.05mm, and mass-produced products pass solderability, thermal shock, heat shock resistance, and gold wire and ball thrust tests.

[0004] Although the above scheme has the above advantages, the traditional method of manufacturing the packaging substrate of the LED electronic display screen usually uses manual layout and manual adjustment of parameters, which is prone to problems such as uneven layout and serious material waste; and in the drilling and electroplating processes, there is a lack of real-time monitoring and adjustment, resulting in the inability to effectively guarantee the hole position accuracy and coating quality. In addition, the production process of the traditional method is relatively cumbersome, relying on experience and manual operation, and is prone to human errors, affecting production efficiency and product quality. Therefore, there is an urgent need for a method for manufacturing the packaging substrate of the LED electronic display screen to solve such problems. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a method for manufacturing a packaging substrate for an LED electronic display screen, which solves the problems of manual typesetting and manual parameter adjustment in the existing technology, which easily lead to uneven typesetting and serious material waste.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] The present invention provides a method for manufacturing a packaging substrate for an LED electronic display screen, comprising:

[0008] Step 1. Perform CNC cutting, integrate the material requirement planning (MRP) system with the CNC cutting equipment, and perform intelligent layout based on production orders and material inventory;

[0009] Step 2. Perform intelligent drilling, monitor the drilling process in real time, and make real-time adjustments based on the monitoring results using machine learning and image recognition technology. The system also determines drilling parameters, including rotational speed, tool entry speed, and tool exit speed, to optimize hole position accuracy and hole shape consistency. It also detects and corrects hole position deviations.

[0010] Step 3. Perform intelligent plating, using intelligent control algorithms to control the electroplating process. During electroplating, the temperature, concentration, and current density parameters of the plating solution are monitored and adjusted in real time. The parameters of the electroplating process are automatically adjusted based on the feedback data to adjust the uniformity and adhesion of the electroplated layer.

[0011] Step 4. Pressing and browning to adjust the surface flatness and corrosion resistance of the substrate;

[0012] Step 5. Resin grinding and finished product cleaning, use an automatic grinding machine to grind the resin to ensure the flatness of the substrate surface.

[0013] The present invention is further configured as follows: the intelligent typesetting method in step 1 is:

[0014] Obtain production order information O, including part quantity, size, and demand date, and material inventory S, including available substrate quantity, size, and material type, through the material requirement planning (MRP) system;

[0015] Preprocess and clean the data to remove invalid data and duplicates;

[0016] The present invention is further configured as follows: the intelligent typesetting method in step 1 further includes:

[0017] Build an intelligent layout algorithm based on genetic algorithm, aiming to maximize the utilization of substrate materials and use binary code to represent substrate layout;

[0018] Assume that the number of parts that each substrate j can accommodate is C j, the size of each part i is L i ×W i , the typesetting scheme X is expressed as (x ij ), where x ij Indicates whether part i is placed on substrate j, with a value of 0 or 1;

[0019] The optimization goal is to maximize the utilization ratio U, which is the ratio of the total part area to the total substrate area;

[0020] The present invention is further configured as follows: in step 1, the optimization objective of maximizing utilization is specifically:

[0021]

[0022] The constraint is that each part can only be placed on one substrate:

[0023] The total area of ​​each base plate cannot exceed its capacity: Where U is the utilization rate, which represents the ratio of the total area of ​​all placed parts to the total area of ​​all substrates, and is used to measure the utilization efficiency of the substrate. M is the number of parts, which represents the number of parts that need to be processed in the production order. N is the number of substrates, which represents the number of substrates that can be used for typesetting. ij is a binary variable, indicating whether part i is placed on substrate j, L i and W i Represent the length and width of part i, L j and W j is the length and width of substrate j, i is the index of the part, ranging from 1 to M, j is the index of the substrate, ranging from 1 to N;

[0024] The present invention is further configured as follows: in step 2, an intelligent drilling adjustment system is constructed to perform intelligent drilling, specifically:

[0025] Use high-speed cameras and sensors to monitor the drilling process in real time, collecting information on hole position deviation d, hole shape consistency c, and surface quality q;

[0026] Preprocess the collected data, including denoising, filtering and data cleaning;

[0027] Define the state space S, including monitoring parameters d, c, q, and extract additional features based on the actual application scenario, including ambient temperature T and drill wear degree W;

[0028] Define the action space A, including the adjustment of drilling parameters, rotation speed r, cutting speed v, retraction speed u, and the change of drill diameter D;

[0029] Define the reward function R to measure the quality and efficiency of each action, which is the weighted sum of processing quality;

[0030] Use a neural network model to model the state space S and action space A, and build an intelligent drilling adjustment system based on deep Q learning DQN;

[0031] Use historical data to train the model and use reinforcement learning algorithms to optimize model parameters;

[0032] Apply the trained model to the actual drilling process, monitor the processing status in real time, and adjust the drilling parameters based on the model output;

[0033] The present invention is further configured as follows: a method for constructing an intelligent drilling adjustment system based on deep Q learning DQN includes:

[0034] Build a deep Q learning DQN neural network model to approximate the action-value function Q(s,a), where s is the state and a is the action;

[0035] The input layer of the neural network model takes the monitoring parameters as input, the hidden layer uses the multi-layer perceptron MLP structure, and the output layer outputs the Q value of each action;

[0036] Use the mean square error (MSE) loss function to compare the predicted Q value with the target Q value, and update the model parameters through the back propagation algorithm;

[0037] Use the ε greedy strategy to balance exploration and exploitation, where ε is a positive number less than 1. At each time step, a random action is selected with probability ε, and the action with the highest Q value is selected with probability 1-ε.

[0038] The present invention is further configured as follows: a method for constructing an intelligent drilling adjustment system based on deep Q learning DQN also includes:

[0039] Calculate the Q value Q(s,a) of executing action a under the current state s and obtain the predicted value through the neural network model;

[0040] Calculate the target Q value Q target (s,a) is used to update the neural network parameters according to the update rule of the Bellman equation: where r is the reward and γ is the discount factor

[0041] ”'

[0042] Zi, s is the next state, a is the action under s;

[0043] The present invention is further configured as follows: a method for constructing an intelligent drilling adjustment system based on deep Q learning DQN also includes:

[0044] By interacting with the environment, experience data e is collected, including state, action, reward, and next state;

[0045] The experience data is stored in the experience replay buffer and randomly sampled from it to reduce the correlation between the data;

[0046] Use stochastic gradient descent (SGD) to update the parameters of the neural network model through the back-propagation algorithm;

[0047] The present invention is further configured as follows: the electroplating process control method is:

[0048] Conduct electroplating parameter monitoring and data collection, monitoring the temperature T, concentration C and current density I during the electroplating process;

[0049] Build a deep learning-based neural network model to model and predict the dynamic changes in the electroplating process. The model uses a convolutional neural network (CNN) combined with a long short-term memory (LSTM) architecture.

[0050] The input of the neural network model is the monitoring data X = {T, C, I} during the electroplating process, and the output of the neural network model is the dynamically adjusted electroplating parameters;

[0051] The neural network model was trained and optimized using the Advantage Action-Critics algorithm (A2C), and the electroplating parameters were adjusted by maximizing the reward function using a reinforcement learning framework.

[0052] The present invention is further configured as follows: the electroplating process control method further includes:

[0053] The reward function R(A) is designed to measure the quality and efficiency of electroplating parameter adjustment: Where: w U 、w F 、 are weight coefficients of uniformity, adhesion and processing time respectively, U represents the uniformity of the electroplating layer, F represents the adhesion of the electroplating layer, T P Indicates the processing time of electroplating.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The present invention integrates the material requirement planning system with CNC cutting equipment and an intelligent typesetting algorithm based on genetic algorithms to achieve automated typesetting and maximize material utilization, significantly reducing material waste and improving production efficiency.

[0056] The present invention uses an intelligent adjustment system based on deep learning during the drilling and electroplating processes to achieve real-time monitoring and automatic adjustment, effectively improving hole position accuracy, hole shape consistency, and the uniformity and adhesion of the electroplating layer.

[0057] The invention solves the problems of manual typesetting and manual parameter adjustment in the prior art, which easily lead to uneven typesetting and serious waste of materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of the method for manufacturing a packaging substrate for an LED electronic display screen of the present invention. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described 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 making creative efforts should fall within the scope of protection of the present invention.

[0060] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0061] The present invention is described in further detail below with reference to the accompanying drawings:

[0062] Example

[0063] See also Figure 1 The present invention provides a method for manufacturing a packaging substrate for an LED electronic display screen, comprising:

[0064] Step 1. Perform CNC cutting, integrate the material requirement planning (MRP) system with the CNC cutting equipment, and perform intelligent layout based on production orders and material inventory to maximize the use of substrate materials and reduce waste;

[0065] The intelligent layout method in step 1 is as follows: Production order information O, including part quantity, size, and demand date, and material inventory S, including available substrate quantity, size, and material type, are obtained through the material requirement planning (MRP) system;

[0066] Preprocess and clean the data to remove invalid data and duplicates;

[0067] Build an intelligent layout algorithm based on genetic algorithm, aiming to maximize the utilization of substrate materials and use binary code to represent substrate layout;

[0068] Assume that the number of parts that each substrate j can accommodate is C j , the size of each part i is L i ×W i , the typesetting scheme X is expressed as (x ij ), where x ij Indicates whether part i is placed on substrate j, with a value of 0 or 1;

[0069] The optimization goal is to maximize the utilization ratio U, which is the ratio of the total part area to the total substrate area;

[0070] The optimization goal of maximizing utilization is as follows:

[0071] The constraint is that each part can only be placed on one substrate:

[0072] The total area of ​​each base plate cannot exceed its capacity: Where U is the utilization rate, which represents the ratio of the total area of ​​all placed parts to the total area of ​​all substrates, and is used to measure the utilization efficiency of the substrate. M is the number of parts, which represents the number of parts that need to be processed in the production order. N is the number of substrates, which represents the number of substrates that can be used for typesetting. ij is a binary variable, indicating whether part i is placed on substrate j, L i and W i Represent the length and width of part i, L j and W j is the length and width of substrate j, i is the index of the part, ranging from 1 to M, j is the index of the substrate, ranging from 1 to N;

[0073] The objective function aims to maximize substrate utilization. Constraints ensure that each part can only be placed on one substrate and that the total area of ​​each substrate cannot exceed its capacity. Finally, a genetic algorithm is used for intelligent layout, iteratively finding the optimal solution to maximize substrate material utilization and reduce waste.

[0074] Step 2. Perform intelligent drilling, monitor the drilling process in real time, and make real-time adjustments based on the monitoring results using machine learning and image recognition technology. The system also determines drilling parameters, including rotational speed, tool entry speed, and tool retraction speed, to optimize hole position accuracy and hole shape consistency. It also detects and corrects hole position deviations, combining machine learning and real-time control algorithms to achieve intelligent optimization and real-time adjustments to the drilling process.

[0075] In step 2, an intelligent drilling adjustment system is constructed to perform intelligent drilling. Specifically:

[0076] Use high-speed cameras and sensors to monitor the drilling process in real time, collecting information on hole position deviation d, hole shape consistency c, and surface quality q;

[0077] Preprocess the collected data, including denoising, filtering and data cleaning;

[0078] Define the state space S, including monitoring parameters d, c, q, and extract additional features based on the actual application scenario, including ambient temperature T and drill wear degree W;

[0079] Define the action space A, including the adjustment of drilling parameters, rotation speed r, cutting speed v, retraction speed u, and the change of drill diameter D;

[0080] Define the reward function R to measure the quality and efficiency of each action, which is the weighted sum of processing quality;

[0081] Use a neural network model to model the state space S and action space A, and build an intelligent drilling adjustment system based on deep Q learning DQN;

[0082] Use historical data to train the model and use reinforcement learning algorithms to optimize model parameters;

[0083] Apply the trained model to the actual drilling process, monitor the processing status in real time, and adjust the drilling parameters according to the model output to ensure hole position accuracy and hole shape consistency;

[0084] Build a deep Q learning DQN neural network model to approximate the action-value function Q(s,a), where s is the state and a is the action;

[0085] The input layer of the neural network model takes the monitoring parameters as input, the hidden layer uses the multi-layer perceptron MLP structure, and the output layer outputs the Q value of each action;

[0086] Use the mean square error (MSE) loss function to compare the predicted Q value with the target Q value, and update the model parameters through the back propagation algorithm;

[0087] Use the ε greedy strategy to balance exploration and exploitation, where ε is a positive number less than 1. At each time step, a random action is selected with probability ε, and the action with the highest Q value is selected with probability 1-ε.

[0088] Calculate the Q value Q(s,a) of executing action a under the current state s and obtain the predicted value through the neural network model;

[0089] Calculate the target Q value Q target(s,a) is used to update the neural network parameters according to the update rule of the Bellman equation: Where r is the reward, γ is the discount factor, s' is the next state, and a' is the action under s';

[0090] Other ways to build an intelligent drilling adjustment system based on deep Q learning DQN include:

[0091] By interacting with the environment, experience data e is collected, including state, action, reward, and next state;

[0092] The experience data is stored in the experience replay buffer and randomly sampled from it to reduce the correlation between the data;

[0093] Use stochastic gradient descent (SGD) to update the parameters of the neural network model through the back-propagation algorithm;

[0094] This enables the design and training of an intelligent drilling adjustment system based on deep Q-learning to optimize the parameter settings of the drilling process;

[0095] Step 3. Perform intelligent plating, using intelligent control algorithms to control the electroplating process. During electroplating, the temperature, concentration, and current density parameters of the plating solution are monitored and adjusted in real time. The parameters of the electroplating process are automatically adjusted based on the feedback data to adjust the uniformity and adhesion of the electroplated layer.

[0096] The electroplating process control method is:

[0097] Conduct electroplating parameter monitoring and data collection, monitoring the temperature T, concentration C and current density I during the electroplating process;

[0098] Build a deep learning-based neural network model to model and predict the dynamic changes in the electroplating process. The model uses a convolutional neural network (CNN) combined with a long short-term memory (LSTM) architecture.

[0099] The input of the neural network model is the monitoring data X = {T, C, I} during the electroplating process, and the output of the neural network model is the dynamically adjusted electroplating parameters;

[0100] The neural network model is trained and optimized using the dominant action-criteria algorithm A2C. The reinforcement learning framework is used to adjust the electroplating parameters by maximizing the reward function to improve the uniformity and adhesion of the electroplated layer.

[0101] The reward function R(A) is designed to measure the quality and efficiency of electroplating parameter adjustment: U 、w F 、 are weight coefficients of uniformity, adhesion and processing time respectively, U represents the uniformity of the electroplating layer, F represents the adhesion of the electroplating layer, TP Indicates the processing time of electroplating;

[0102] Step 4. Pressing and browning to adjust the surface flatness and corrosion resistance of the substrate;

[0103] Step 5. Resin grinding and finished product cleaning, use an automatic grinding machine to grind the resin to ensure the flatness of the substrate surface.

[0104] The present invention integrates the material requirement planning (MRP) system with CNC cutting equipment and utilizes an intelligent typesetting algorithm based on a genetic algorithm to maximize the utilization of substrate materials and reduce waste. Secondly, in the intelligent drilling stage, an intelligent drilling adjustment system constructed with a deep Q learning algorithm is adopted to achieve real-time adjustment and optimization of drilling parameters, ensuring hole position accuracy and hole shape consistency. In the intelligent plating process, a neural network model based on deep learning is used to monitor and adjust the electroplating parameters in real time to improve the uniformity and adhesion of the electroplating layer. Finally, in the pressing and browning and resin grinding stages, an automatic plate grinding machine is used for resin grinding to ensure the flatness of the substrate surface.

[0105] The above content is only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for manufacturing a packaging substrate for an LED electronic display screen, characterized in that: include: Step 1. Perform CNC cutting, integrate the material requirement planning (MRP) system with the CNC cutting equipment, and perform intelligent layout based on production orders and material inventory; Step 2. Perform intelligent drilling, monitor the drilling process in real time, and make real-time adjustments based on the monitoring results using machine learning and image recognition technology. The system also determines drilling parameters, including rotational speed, tool entry speed, and tool exit speed, to optimize hole position accuracy and hole shape consistency. It also detects and corrects hole position deviations. Step 3. Perform intelligent plating, using intelligent control algorithms to control the electroplating process. During electroplating, the temperature, concentration, and current density parameters of the plating solution are monitored and adjusted in real time. The parameters of the electroplating process are automatically adjusted based on the feedback data to adjust the uniformity and adhesion of the electroplated layer. Step 4. Pressing and browning to adjust the surface flatness and corrosion resistance of the substrate; Step 5. Resin grinding and finished product cleaning, use an automated grinding machine to grind the resin to ensure the surface flatness of the substrate; The smart typesetting method in step 1 is: Obtain production order information through the Material Requirements Planning (MRP) system , including part quantity, size and required date, as well as material inventory , including the number, size, and material types of available substrates; Preprocess and clean the data to remove invalid data and duplicates; Build an intelligent layout algorithm based on genetic algorithm, aiming to maximize the utilization of substrate materials and use binary code to represent substrate layout; Each substrate The number of parts that can be accommodated is , each part The size is , typesetting scheme Expressed as ,in Indicates parts Is it placed on the substrate On, the value is 0 or 1; The optimization goal is to maximize utilization , which is the ratio of the total part area to the total substrate area; In step 1, the optimization objective of maximizing utilization is specifically: ； The constraint is that each part can only be placed on one substrate: ; The total area of ​​each base plate cannot exceed its capacity: ,in The utilization rate is the ratio of the total area of ​​all placed parts to the total area of ​​all substrates, which is used to measure the utilization efficiency of the substrate. is the number of parts, indicating the number of parts that need to be processed in the production order. is the number of substrates, indicating the number of substrates that can be used for typesetting. is a binary variable representing the part Is it placed on the substrate superior, and Represent parts separately length and width, and For substrate length and width, The index of the part, ranging from 1 to , The index of the substrate, ranging from 1 to .

2. The method for manufacturing a packaging substrate for an LED electronic display according to claim 1, characterized in that: In step 2, an intelligent drilling adjustment system is constructed to perform intelligent drilling. Specifically: Use high-speed cameras and sensors to monitor the drilling process in real time and collect hole position deviations , hole shape consistency , surface quality ; Preprocess the collected data, including denoising, filtering and data cleaning; Define the state space , including monitoring parameters 、 、 , and extract additional features according to the actual application scenario, including ambient temperature , drill bit wear degree ; Defining the action space , including the adjustment of drilling parameters, speed , cutting speed , retraction speed , and drill diameter replacement; Define the reward function Measure the quality and efficiency of each action as the weighted sum of processing quality; Use neural network model to analyze state space and action space Conduct modeling and build an intelligent drilling adjustment system based on deep Q learning DQN; Use historical data to train the model and use reinforcement learning algorithms to optimize model parameters; The trained model is applied to the actual drilling process, the processing status is monitored in real time, and the drilling parameters are adjusted according to the model output.

3. The method for manufacturing a packaging substrate for an LED electronic display according to claim 2, characterized in that: The methods for building an intelligent drilling adjustment system based on deep Q learning DQN include: Build a deep Q learning DQN neural network model to approximate the action value function ,in It's a state. It is an action; The input layer of the neural network model takes the monitoring parameters as input, the hidden layer uses the multi-layer perceptron MLP structure, and the output layer outputs the value; Using the mean square error MSE loss function, the predicted Values ​​and Goals The values ​​are compared and the model parameters are updated through the back propagation algorithm; Use the ε greedy strategy to balance exploration and exploitation, where ε is a positive number less than 1. At each time step, a random action is selected with probability ε and with probability The action with the highest Q-value is selected.

4. The method for manufacturing a packaging substrate for an LED electronic display according to claim 3, wherein: Other ways to build an intelligent drilling adjustment system based on deep Q learning DQN include: Calculate the current state Next action Q value , get the predicted value through the neural network model; Calculate the target Q value Used to update the neural network parameters according to the update rule of the Bellman equation: ,in It's a reward. is the discount factor, is the next state, is Next action.

5. The method for manufacturing a packaging substrate for an LED electronic display screen according to claim 4, wherein: Other ways to build an intelligent drilling adjustment system based on deep Q learning DQN include: Collecting empirical data through interaction with the environment , including state, action, reward and next state; The experience data is stored in the experience replay buffer and randomly sampled from it to reduce the correlation between the data; Stochastic gradient descent (SGD) is used to update the parameters of the neural network model through the back-propagation algorithm.

6. The method for manufacturing a packaging substrate for an LED electronic display screen according to claim 5, characterized in that: The electroplating process control method is: Conduct electroplating parameter monitoring and data collection, and monitor the temperature during the electroplating process ,concentration and current density ; Build a deep learning-based neural network model to model and predict dynamic changes in the electroplating process. The model uses a convolutional neural network (CNN) combined with a long short-term memory (LSTM) architecture. The neural network model input is the monitoring data of the electroplating process , the output of the neural network model is the dynamically adjusted electroplating parameters; The neural network model is trained and optimized using the advantage action-critic algorithm A2C, and the electroplating parameters are adjusted by maximizing the reward function using a reinforcement learning framework.

7. The method for manufacturing a packaging substrate for an LED electronic display according to claim 6, characterized in that: Electroplating process control methods also include: Designing the reward function Measuring the quality and efficiency of electroplating parameter adjustments: ,in: 、 、 are the weight coefficients of uniformity, adhesion and processing time, Indicates the uniformity of the electroplating layer. Indicates the adhesion of the electroplating layer, Indicates the processing time of electroplating.

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