A production management system for high-generation substrate glass

Abstract

The application discloses a kind of production management systems of high generation substrate glass, it is related to intelligent manufacturing technical field, generate each piece of substrate glass to generate virtual Glass ID by identity management module, substrate glass processing data and equipment real-time running state data are collected by data acquisition module, state evaluation coefficient is obtained by state evaluation calculation and assignment based on substrate glass processing data and equipment real-time running state data through data processing module, equipment state evaluation coefficient is input into the comprehensive production evaluation model established in advance by control center, and the comprehensive production evaluation result is obtained, and the unqualified substrate glass is screened out by comparing with preset threshold value, the virtual Glass ID corresponding to the unqualified substrate glass is marked, and the position of the unqualified substrate glass is positioned according to the marked virtual Glass ID, and then optimized, which can manage and optimize the production process according to real-time equipment state and substrate glass production state.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, specifically a production management system for high-generation substrate glass. Background Technology

[0002] As flat panel display technology advances towards larger sizes and ultra-high definition, G8.5 and higher generation glass substrates are becoming mainstream. High-generation substrates are characterized by large area, complex processes, expensive production equipment, and stringent yield management. Traditional production management methods, such as reliance on paper documents and decentralized data recording systems, can no longer meet the high demands of modern intelligent manufacturing for real-time data accuracy and traceability.

[0003] Traditional management systems suffer from poor coordination between stations, production rhythm relies on experience-based judgment, and cannot be dynamically optimized based on real-time equipment usage data. This results in the overall production line speed failing to reach its theoretical maximum, creating a bottleneck for capacity improvement. Summary of the Invention

[0004] To address the shortcomings mentioned in the background section, the present invention aims to provide a production management system for high-generation substrate glass, which can manage and optimize the production process based on real-time equipment status and substrate glass production status.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A production management system for high-generation substrate glass includes:

[0007] The data acquisition module collects substrate glass processing data and real-time equipment operating status data, and sends them to the data processing module.

[0008] The data processing module evaluates and calculates the substrate glass processing data and the real-time operating status data of the equipment, derives the production management operation evaluation coefficient, and sends it to the control center.

[0009] The control center inputs the production management operation evaluation coefficients into the pre-established comprehensive production evaluation model to calculate the comprehensive production evaluation results, determine whether the substrate glass is qualified, mark the unqualified substrate glass, and send it to the dynamic optimization module.

[0010] The dynamic optimization module optimizes the management of the equipment that marks the locations of defective substrate glass.

[0011] Furthermore, the data processing module marks the substrate glass processing data and the real-time operating status data of the equipment, performs partial status evaluation calculations on the substrate glass based on the marked substrate glass processing data to obtain the substrate glass status evaluation coefficient, performs partial status evaluation calculations on the equipment based on the marked real-time operating status data of the equipment to obtain the equipment status evaluation coefficient, and assigns a proportional value to the substrate glass status evaluation coefficient and the equipment status evaluation coefficient to obtain the production management operation evaluation coefficient.

[0012] Furthermore, the data processing module marks the substrate glass processing data and the real-time operating status data of the equipment, including:

[0013] The substrate glass thickness data is marked as The roughness data of the substrate glass is marked as Mark the equipment temperature data as Mark the equipment pressure data as Mark the device operating load data as ,in, This is the identifier of the virtual Glass ID corresponding to the substrate glass, and , This represents the total number of virtual Glass IDs corresponding to the substrate glass.

[0014] Furthermore, the process by which the data processing module calculates the substrate glass condition evaluation coefficient includes:

[0015] Using formula

[0016] The substrate glass condition evaluation coefficient was calculated. In the formula, The preset substrate glass thickness coefficient, The preset roughness coefficient of the substrate glass. and All are preset correlation coefficients, and > .

[0017] Furthermore, the process by which the data processing module calculates the equipment status evaluation coefficient includes:

[0018] Use the formula {G2}_{i}=\left [ {\frac {{K}_{1}\left ( {{P}_{i}-{P}_{0}} \right )+{K}_{2}\left ( {{F}_{i}-{F}_{0}} \right )} {1+{K}_{3}\left ( {{T}_{i}-{T}_{0}} \right )}} \right ]\sum ^{n}_{i=1} \left ( {\frac {{T}_{i}+{P}_{i}+{F}_{i}} {{K}_{1}+{K}_{2}+{K}_{3}}} \right )

[0019] Calculate the equipment condition assessment coefficient In the formula, For standard equipment temperature coefficient, The pressure coefficient of standard equipment. The standard equipment operating load factor, The temperature influence coefficient of the equipment. This is the equipment pressure influence coefficient. This is the influence coefficient of equipment operating load.

[0020] Furthermore, the data processing module is based on the substrate glass condition evaluation coefficient. and equipment condition assessment coefficient Perform proportional assignment using the formula

[0021] Calculate the production management operation evaluation coefficient In the formula, This is the preset scaling factor.

[0022] Furthermore, the control center inputs the production management operation evaluation coefficient into the pre-established comprehensive production evaluation model, outputs the comprehensive production evaluation result, and compares the comprehensive production evaluation result with the preset production evaluation threshold. If the result is less than the preset production evaluation threshold, it is considered unqualified substrate glass; otherwise, it is considered qualified substrate glass.

[0023] Furthermore, the training process of the comprehensive production evaluation model is carried out using an artificial intelligence model, wherein the artificial intelligence model is an RBF neural network model;

[0024] The training process of the artificial intelligence model includes:

[0025] The processing data and operating status data are obtained from the server. The processing data, operating status data and preset production assessment thresholds are used to train the RBF neural network model to improve the accuracy of production assessment status. The comprehensive production assessment model is then obtained and stored.

[0026] Furthermore, the control center marks the virtual Glass IDs corresponding to the defective substrate glass using the virtual Glass IDs of all stored substrate glass, and sends the marked virtual Glass IDs and defective signals to the dynamic optimization module.

[0027] Furthermore, the virtual Glass IDs of all substrate glass stored in the control center are pre-managed and entered through the identity management module, including:

[0028] When the A-frame is put into the production line, a virtual Glass ID is generated for each substrate glass on the A-frame. Each substrate glass corresponds to a unique virtual Glass ID, which is a unique identifier for the substrate glass in the production process.

[0029] A production management method for high-generation substrate glass, the method comprising the following steps:

[0030] Data on substrate glass processing and real-time equipment operation status are collected and labeled. The substrate glass processing data includes substrate glass thickness data and substrate glass roughness data; the real-time equipment operation status data includes equipment temperature data, equipment power data, and equipment operating load data.

[0031] Based on the marked substrate glass processing data, the state evaluation of the substrate glass is calculated to obtain the substrate glass state evaluation coefficient. Based on the marked real-time equipment operation status data, the state evaluation of the equipment is calculated to obtain the equipment state evaluation coefficient. Based on the substrate glass state evaluation coefficient and the equipment state evaluation coefficient, a proportional value is assigned to obtain the production management operation evaluation coefficient.

[0032] The production management operation evaluation coefficient is input into the pre-established comprehensive production evaluation model, and the comprehensive production evaluation result is output. The comprehensive production evaluation result is compared with the preset production evaluation threshold. If it is less than the preset production evaluation threshold, it is a substandard substrate glass; otherwise, it is a qualified substrate glass.

[0033] Obtain the virtual Glass IDs of all pre-generated substrate glasses, mark the virtual Glass IDs corresponding to defective substrate glasses, locate the position of defective substrate glasses based on the marked virtual Glass IDs, and optimize the parameters of the equipment at the position of defective substrate glasses.

[0034] The beneficial effects of this invention are:

[0035] The high-generation substrate glass production management system provided by this invention generates a virtual Glass ID for each substrate glass through an identity management module, collects substrate glass processing data and real-time equipment operating status data through a data acquisition module, performs status evaluation calculations based on the substrate glass processing data and real-time equipment operating status data through a data processing module, and assigns values ​​to obtain equipment status evaluation coefficients. The control center inputs the equipment status evaluation coefficients into a pre-established comprehensive production evaluation model to obtain comprehensive production evaluation results. By comparing with preset thresholds, unqualified substrate glass is screened out. The virtual Glass IDs corresponding to the unqualified substrate glass are marked, the location of the unqualified substrate glass is located based on the marked virtual Glass IDs, and the equipment used at the location of the unqualified substrate glass is optimized. This enables the management and optimization of the production process based on real-time equipment status and substrate glass production status. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of the system structure of the present invention.

[0038] Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] like Figure 1 As shown, the present invention discloses a production management system for high-generation substrate glass, including: an identity management module, a data acquisition module, a data processing module, a dynamic optimization module, and a control center.

[0041] The identity management module is used to generate a virtual Glass ID for each substrate glass on the A-frame when it is put into the production line. Each substrate glass corresponds to a unique virtual Glass ID.

[0042] An identity is established by generating a unique virtual Glass ID for each substrate glass to distinguish each substrate glass.

[0043] The virtual Glass ID is a unique identifier for the substrate glass in the production process. All reported production process data is associated and indexed through this virtual Glass ID. In subsequent production management, the virtual Glass ID is used to locate and report qualified or unqualified substrate glass, thus enabling subsequent location management.

[0044] The generated virtual Glass ID is sent to the control center for storage.

[0045] The data acquisition module is used to collect substrate glass processing data and real-time equipment operating status data, and send the collected substrate glass related data to the data processing module for processing. Specifically, the substrate glass processing data (Trace Data) includes: substrate glass thickness data and substrate glass roughness data; the real-time equipment operating status data includes equipment temperature data, equipment power data, and equipment operating load data.

[0046] Specifically, equipment power data is used to determine whether the equipment is operating efficiently and thus to determine its operating status; equipment operating load data is used to determine the cumulative usage of the equipment and, based on this cumulative usage, to determine whether the equipment is overloaded and thus to determine its operating status.

[0047] In this embodiment, the substrate glass thickness data is acquired and measured using a non-contact thickness measuring instrument, specifically a laser scanning thickness gauge. The thickness is determined by emitting a laser to the substrate glass surface and a reference surface, and then measuring the optical path difference. The equipment operating load data is acquired and measured using a non-contact surface topography measuring instrument, specifically a laser scattering roughness measuring instrument. The roughness is determined by the distribution of the scattered light spot after the laser is emitted to the substrate glass surface. Temperature data is acquired using a temperature sensor, and current and voltage data are acquired using current and voltage sensors. Finally, the equipment power is obtained based on the current and voltage, representing the equipment's operating efficiency, i.e., the equipment power data. The operating load time is acquired using a counter.

[0048] After receiving the substrate glass processing data and real-time equipment operating status data sent by the data acquisition module, the data processing module performs data processing. Specifically, the data processing module's processing procedure includes the following steps:

[0049] The substrate glass processing data and real-time equipment operating status data are marked, and the specific process is as follows:

[0050] The substrate glass thickness data is marked as The roughness data of the substrate glass is marked as Mark the equipment temperature data as Mark the equipment pressure data as Mark the device operating load data as ,in, This is the identifier of the virtual Glass ID corresponding to the substrate glass, and , This represents the total number of virtual Glass IDs corresponding to the substrate glass.

[0051] Based on the marked substrate glass processing data, a partial state assessment calculation of the substrate glass is performed to obtain the substrate glass state assessment coefficient. The calculation process is as follows:

[0052] Using formula

[0053] The substrate glass condition evaluation coefficient was calculated. In the formula, The preset substrate glass thickness coefficient, The preset roughness coefficient of the substrate glass, and All are preset correlation coefficients, and > .

[0054] In this embodiment, the preset substrate glass thickness coefficient is the thickness value that the substrate glass will reach after final processing, which is preset before processing; the preset substrate glass roughness coefficient is the roughness value that the substrate glass will reach after final processing, which is preset before processing; and the preset correlation coefficient is a coefficient obtained by comprehensively processing some parameters related to the substrate glass thickness and substrate glass roughness.

[0055] Based on the labeled real-time operating status data of the equipment, a partial status assessment of the equipment is calculated to obtain the equipment status assessment coefficient. The calculation process is as follows:

[0056] Use the formula {G2}_{i}=\left [ {\frac {{K}_{1}\left ( {{P}_{i}-{P}_{0}} \right )+{K}_{2}\left ( {{F}_{i}-{F}_{0}} \right )} {1+{K}_{3}\left ( {{T}_{i}-{T}_{0}} \right )}} \right ]\sum ^{n}_{i=1} \left ( {\frac {{T}_{i}+{P}_{i}+{F}_{i}} {{K}_{1}+{K}_{2}+{K}_{3}}} \right )

[0057] The equipment condition assessment coefficient was calculated. In the formula, For standard equipment temperature coefficient, The standard equipment pressure coefficient, The standard equipment operating load factor, The temperature influence coefficient of the equipment. This is the equipment pressure influence coefficient. This is the equipment operating load impact coefficient.

[0058] Furthermore, in the specific implementation process, the standard equipment temperature coefficient, standard equipment pressure coefficient, and standard equipment operating load coefficient are obtained by repeatedly simulating and calculating the average values ​​of the collected equipment temperature data, equipment power data, and equipment operating load data.

[0059] In this embodiment, the equipment temperature influence coefficient, equipment pressure influence coefficient, and equipment operating load influence coefficient are calculated by comprehensively evaluating the influence of external factors when the equipment temperature data, equipment power data, and equipment operating load data are obtained daily. These factors include human factors, machine testing, and environmental factors, etc. Human factors refer to those caused by improper human operation or use.

[0060] Based on substrate glass condition evaluation coefficient and equipment condition assessment coefficient By assigning proportional values, the production management operation evaluation coefficient is obtained. The proportional values ​​are assigned as follows:

[0061] ;

[0062] In the formula, This is the preset scaling factor.

[0063] The calculated production management operation evaluation coefficient Send to the control center.

[0064] The control center receives the production management operation evaluation coefficient sent by the data processing module. Subsequently, the production management process of the substrate glass is evaluated based on the production management operation evaluation coefficient. The optimization direction is determined based on the evaluation results. Furthermore, the corresponding substrate glass is identified according to the virtual Glass ID corresponding to each production management operation evaluation coefficient, and the corresponding dynamic control signal is sent to the dynamic optimization module. The specific process includes the following steps:

[0065] The production management operation evaluation coefficients are input into the pre-established comprehensive production evaluation model, and the comprehensive production evaluation results are output. The comprehensive production evaluation results are compared with the preset production evaluation threshold. If the results are less than the preset production evaluation threshold, they are unqualified substrate glass; otherwise, they are qualified substrate glass. The virtual Glass ID corresponding to the unqualified substrate glass is marked. The marked virtual Glass ID and the unqualified signal are sent to the dynamic optimization module.

[0066] The training process of the pre-established comprehensive production evaluation model includes:

[0067] The training is performed using an artificial intelligence model, specifically an RBF neural network model.

[0068] The training process of the artificial intelligence model includes:

[0069] Processing data and operating status data are obtained through a server. The processing data includes the thickness and roughness of the substrate glass; the operating status data includes the equipment temperature, equipment power, and equipment operating load.

[0070] The RBF neural network model is trained using processing data, operational status data, and preset production assessment thresholds to improve the accuracy of production assessment. Step-by-step training is performed using K-Means clustering and least squares. A regularized objective function is used to enhance the prediction accuracy and generalization ability of the RBF neural network model. A nonlinear dynamic model is established between the processing data, operational status data, and the response to the preset production assessment thresholds. Based on the input production management and operational assessment coefficients, the most accurate comprehensive production assessment result is quickly predicted, and the comprehensive production assessment model is obtained and stored.

[0071] The preset production evaluation threshold is obtained by taking the weighted average of multiple comprehensive production evaluation results after the control center has processed them multiple times.

[0072] The dynamic optimization module, based on the marked virtual Glass ID and the non-conforming signal sent by the control center, locates the position of the non-conforming substrate glass according to the marked virtual Glass ID, optimizes the equipment at the position of the non-conforming substrate glass, sends production rhythm optimization parameters, and optimizes the operating status of the production equipment.

[0073] like Figure 2 As shown, this invention also discloses a high-speed production management method for high-generation substrate glass, the method comprising the following steps:

[0074] S101: Collect substrate glass processing data and equipment real-time operating status data, and mark the substrate glass processing data and equipment real-time operating status data. The substrate glass processing data includes: substrate glass thickness data and substrate glass roughness data; the equipment real-time operating status data includes equipment temperature data, equipment power data and equipment operating load data.

[0075] S102: Based on the marked substrate glass processing data, perform partial state evaluation calculations for the substrate glass to obtain the substrate glass state evaluation coefficient; based on the marked real-time equipment operating status data, perform partial state evaluation calculations for the equipment to obtain the equipment state evaluation coefficient; based on the substrate glass state evaluation coefficient and the equipment state evaluation coefficient, assign proportional values ​​to obtain the production management operation evaluation coefficient.

[0076] S103: Input the production management operation evaluation coefficient into the pre-established comprehensive production evaluation model, output the comprehensive production evaluation result, compare the comprehensive production evaluation result with the preset production evaluation threshold, and the glass substrate is unqualified if it is less than the preset production evaluation threshold; otherwise, it is qualified.

[0077] S104: Obtain the virtual Glass IDs of all pre-generated substrate glasses, mark the virtual Glass IDs corresponding to the defective substrate glasses, locate the position of the defective substrate glass based on the marked virtual Glass IDs, and optimize the parameters of the equipment at the position of the defective substrate glass.

[0078] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0079] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0080] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0081] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0082] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

Claims

1. A production management system for high-generation substrate glass, characterized by, The method comprises the following steps: The data acquisition module acquires substrate glass processing data and equipment real-time running state data and sends them to the data processing module; The substrate glass processing data comprises substrate glass thickness data and substrate glass roughness data; The data processing module evaluates and calculates the substrate glass processing data and the equipment real-time running state data, obtains a production management running evaluation coefficient, and sends it to the control center; The data processing module marks the substrate glass processing data and the equipment real-time running state data, evaluates and calculates the substrate glass state based on the marked substrate glass processing data, obtains a substrate glass state evaluation coefficient, evaluates and calculates the equipment state based on the marked equipment real-time running state data, obtains an equipment state evaluation coefficient, performs proportional assignment based on the substrate glass state evaluation coefficient and the equipment state evaluation coefficient, and obtains a production management running evaluation coefficient after weighted calculation; The control center inputs the production management running evaluation coefficient into a pre-established comprehensive production evaluation model to obtain a comprehensive production evaluation result, judges whether the substrate glass is qualified, marks the unqualified substrate glass, and sends it to the dynamic optimization module; The dynamic optimization module optimizes the management of the equipment where the unqualified substrate glass is located.

2. The production management system according to claim 1, characterized by, The data processing module marks the substrate glass processing data and the equipment real-time running state data, comprising: The substrate glass thickness data is labeled as The substrate glass roughness data is labeled as The equipment temperature data is labeled as The equipment pressure data is labeled as The equipment run load data is labeled as wherein, is the label of the virtual Glass ID corresponding to the substrate glass, and , is the total number of the virtual Glass IDs corresponding to the substrate glass.

3. The production management system according to claim 2, characterized by, The data processing module calculates the substrate glass state evaluation coefficient, comprising: Using the formula A substrate glass state evaluation coefficient is calculated wherein is a preset substrate glass thickness coefficient, is a preset substrate glass roughness coefficient, and are preset correlation coefficients, and > 0 .

4. The production management system according to claim 3, characterized by The data processing module calculates the equipment state evaluation coefficient, comprising: Using the formula {G2}_{i}=\left [ {\frac {{K}_{1}\left ( {{P}_{i}-{P}_{0}} \right )+{K}_{2}\left ( {{F}_{i}-{F}_{0}} \right )} {1+{K}_{3}\left ( {{T}_{i}-{T}_{0}} \right )}} \right ]\sum ^{n}_{i=1} \left ( {\frac {{T}_{i}+{P}_{i}+{F}_{i}} {{K}_{1}+{K}_{2}+{K}_{3}}} \right ) A device state evaluation coefficient is calculated wherein is a standard device temperature coefficient, is a standard device pressure coefficient, is a standard device operating load coefficient, is a device temperature influence coefficient, is a device pressure influence coefficient, is a device operating load influence coefficient.

5. The production management system according to claim 4, characterized by The data processing module evaluates the substrate glass state based on a substrate glass state evaluation coefficient and an apparatus state evaluation coefficient performs proportional assignment using the formula A production management operation evaluation coefficient is calculated wherein is a preset proportional coefficient.

6. The production management system according to Claim 1, characterized by The control center inputs the production management running evaluation coefficient into a pre-established comprehensive production evaluation model, outputs a comprehensive production evaluation result, compares the comprehensive production evaluation result with a preset production evaluation threshold, and marks the virtual Glass ID corresponding to the unqualified substrate glass through the stored virtual Glass ID of all substrate glasses.

7. The production management system according to claim 6, characterized by The training process of the comprehensive production evaluation model adopts an artificial intelligence model for training, wherein the artificial intelligence model is an RBF neural network model; The training process of the artificial intelligence model comprises: The server acquires processing data and running state data, trains the RBF neural network model with the processing data and the running state data and a preset production evaluation threshold, and obtains and stores a comprehensive production evaluation model to improve the production evaluation state accuracy.

8. The production management system according to claim 7, characterized by, The control center marks the virtual Glass ID corresponding to the unqualified substrate glass through the stored virtual Glass ID of all substrate glasses, and sends the marked virtual Glass ID and an unqualified signal to the dynamic optimization module.

9. The production management system according to claim 8, characterized by, The control center pre-manages and inputs the virtual Glass ID of all substrate glasses through an identity management module, comprising: When the A-type rack is put into the production line, a virtual Glass ID is generated for each piece of substrate glass on the A-type rack, wherein each piece of substrate glass corresponds to a unique virtual Glass ID, and the virtual Glass ID is the unique identifier of the substrate glass in the production process.

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