Panel printing monitoring method and device based on Internet of Things and medium

Abstract

The invention relates to a panel printing monitoring method and device based on the Internet of Things and a medium, and relates to the technical field of the Internet of Things, and the method comprises the steps: obtaining a panel printing process, recognizing a plurality of production key links according to the panel printing process, and constructing key point data according to the plurality of production key links; a current production environment is obtained, production environment data are constructed according to the production environment, and the production environment data at least comprise temperature data and humidity data; constructing an Internet of Things architecture according to the key point data and the production environment data, wherein the Internet of Things architecture at least comprises a plurality of different types of sensors which are respectively arranged in a plurality of production key links and are suitable for the current production environment; acquiring real-time parameter data uploaded by the sensor module in the panel printing process; analyzing according to the real-time parameter data and the key point data to obtain a monitoring result, and judging whether optimization is needed or not according to the monitoring result; when optimization is needed, an optimization scheme is obtained through analysis according to the real-time parameter data and the key point data, and the panel printing process is optimized through the optimization scheme. The method has the effect of achieving accurate control and optimization in the panel printing process.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things technology, and in particular to a panel printing monitoring method, device and medium based on the Internet of Things. Background Art

[0002] In the production of printed circuit boards (PCBs), precise control and optimization of production parameters are crucial for improving product quality and efficiency. Existing technologies for achieving precise control of the panel production process primarily include: 1) traditional manual inspection methods, where operators regularly use handheld measuring instruments to monitor key parameters during production; 2) fixed sensor installations, where sensors are placed in specific locations to monitor parameter changes in a specific area; and 3) monitoring systems based on wired communications, where data from individual sensors is transmitted via cables to a central control room for centralized processing and display.

[0003] However, while these methods can meet production needs to a certain extent, they still lack real-time and comprehensiveness. First, manual inspection methods rely on manual operation, which is not only time-consuming and labor-intensive, but also prone to human error and cannot achieve continuous and real-time monitoring. Second, the limited installation locations of fixed sensors make it difficult to cover all key links in the entire production process, resulting in the failure to effectively monitor some important parameters. In addition, monitoring systems based on wired communication have complex wiring, high maintenance costs, and poor flexibility, making them difficult to adjust and expand. These issues limit the precise control and optimization of the panel printing production process. Summary of the Invention

[0004] The purpose of this application is to provide a panel printing monitoring method, equipment and medium based on the Internet of Things to achieve precise control and optimization of the panel printing process.

[0005] In the first aspect, the present application provides a panel printing monitoring method based on the Internet of Things, which adopts the following technical solutions: A panel printing monitoring method based on the Internet of Things, comprising: Obtain the panel printing process, identify several key production links based on the panel printing process, and construct key point data based on several key production links; Acquire the current production environment, and construct production environment data based on the production environment, wherein the production environment data includes at least temperature data and humidity data; Building an Internet of Things architecture based on key point data and production environment data, the Internet of Things architecture at least including several different types of sensors located at several key production links and suitable for the current production environment; Obtain real-time parameter data uploaded by the sensor module during the panel printing process; Analyze the real-time parameter data and key point data to obtain monitoring results, and determine whether optimization is needed based on the monitoring results; When optimization is required, an optimization plan is obtained based on real-time parameter data and key point data analysis, and the panel printing process is optimized through the optimization plan.

[0006] By adopting the above-mentioned technical solution, this application utilizes Industrial Internet of Things (IIoT) technology and intelligent manufacturing technology, monitors the key links in the panel printing process by integrating different types of sensors, and automatically optimizes the production process based on the monitoring results, thereby achieving precise control and optimization of the panel production process.

[0007] Furthermore, the panel printing process is obtained, and several key production links are identified according to the panel printing process, including: Obtain the impact of each production link in the panel printing process on production efficiency and product quality; Rank each production link in the panel printing process in descending order of their impact on production efficiency and product quality; Select several production links that are ranked first as key production links.

[0008] By adopting the above-mentioned technical solution, since the panel printing process has many production links, this application selects several production links with the greatest impact on production efficiency and product quality as key production links for key monitoring based on the degree of impact of each production link on production efficiency and product quality, which can effectively improve the efficiency of panel printing monitoring and optimization.

[0009] Furthermore, the key point data is constructed based on several key production links, specifically including: Obtain key parameters corresponding to each key production link, wherein the key parameters include at least ink volume, temperature, humidity and pressure; Several key production links and corresponding key parameters are constructed as key point data.

[0010] By adopting the above-mentioned technical solution, this application can perform intelligent optimization in the panel printing process by obtaining key parameters and monitoring and optimizing the key parameters, such as adjusting the ink volume, controlling humidity and temperature, etc., to improve the quality and efficiency of panel printing.

[0011] Furthermore, the construction of the Internet of Things architecture based on key point data and production environment data specifically includes: Obtaining an IoT infrastructure based on key point data, wherein the IoT infrastructure includes sensor types, data transmission methods, and data processing procedures; Determine the environmental compatibility of the IoT infrastructure based on production environment data, including compatibility with existing equipment interfaces, data formats, and communication protocols. The IoT infrastructure is adjusted according to the environmental compatibility to obtain the IoT architecture.

[0012] By adopting the above-mentioned technical solution, this application constructs an Internet of Things architecture through key point data and production environment data, so that the final Internet of Things architecture can adapt to the current production process and production environment, thereby monitoring and optimizing the panel printing process according to actual conditions, and improving the intelligence and efficiency of panel printing.

[0013] Furthermore, the monitoring results obtained by analyzing the real-time parameter data and key point data specifically include: Preset key parameter ranges based on key point data; Compare the real-time parameter data with the key parameter range to obtain the monitoring results.

[0014] By adopting the above technical solution, the present application monitors by comparing the real-time parameter data with the preset range, which can timely reflect the current real-time parameter situation and improve the real-time and efficiency of panel printing monitoring.

[0015] Furthermore, the optimization scheme obtained based on the analysis of real-time parameter data and key point data specifically includes: Construct an initial model for predicting optimization solutions; Acquire real-time parameter data samples, key point data samples and corresponding optimization solution samples during several panel printing optimization processes in the optimization solution database; The real-time parameter data samples and key point data samples are used as input features, and the optimization solution samples are used as output features to construct a sample set; The initial model for predicting the optimization scheme is trained according to the sample set to obtain a trained optimization scheme prediction model; The real-time parameter data and key point data are input into the trained optimization solution prediction model to obtain the optimization solution.

[0016] By adopting the above technical solution, this application constructs and trains an optimization solution prediction model, so that the model can autonomously learn the characteristic relationship between real-time parameter data, key point data and optimization solutions based on big data, ensure the accuracy of the final prediction, and improve the intelligence and efficiency of optimization solution generation.

[0017] Furthermore, it also includes building a feedback mechanism, including: Feedback the monitoring results to the management end or remote end; Obtain feedback result data and adjust the optimization plan based on the feedback result data.

[0018] By adopting the above technical solution, this application builds a feedback mechanism to feed back monitoring results and optimization plans to production management personnel, so as to adjust production strategies in a timely manner, continuously optimize performance, and improve monitoring accuracy and production efficiency.

[0019] Furthermore, it also includes building a visualization mechanism, including: Generate real-time parameter data change trend charts and early warning prompts based on monitoring results; Generate visual signals based on parameter data change trend charts, early warning prompts and optimization plans, and send the visual signals to the visual interface.

[0020] By adopting the above technical solution, this application constructs a visualization mechanism to dim and visualize the monitoring results and optimization solutions, providing real-time monitoring functions, which makes it easier for production managers to quickly understand and adjust production status.

[0021] In a second aspect, the present application provides a computer device that adopts the following technical solution: A computer device includes a memory and a processor, wherein the memory stores a panel printing monitoring method based on the Internet of Things as described in the first aspect, which can be loaded and executed by the processor.

[0022] In a third aspect, the present application provides a computer-readable storage medium that employs the following technical solutions: A computer-readable storage medium stores a computer program that can be loaded by a processor and executed by a panel printing monitoring method based on the Internet of Things as described in the first aspect.

[0023] In summary, this application includes at least one of the following beneficial technical effects: 1. This application utilizes Industrial Internet of Things (IIoT) technology and intelligent manufacturing technology to monitor key links in the panel printing process by integrating different types of sensors. Based on the monitoring results, the production process is automatically optimized, thereby achieving precise control and optimization of the panel production process. 2. This application selects the production links with the greatest impact on production efficiency and product quality as key production links for key monitoring based on the degree of impact of each production link on production efficiency and product quality. This can effectively improve the efficiency of panel printing monitoring and optimization. 3. This application constructs an Internet of Things architecture through key point data and production environment data, so that the final Internet of Things architecture can adapt to the current production process and production environment, thereby monitoring and optimizing the panel printing process according to actual conditions, and improving the intelligence and efficiency of panel printing. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a schematic diagram of the overall method flow of an embodiment of the present application; Figure 2 It is a schematic diagram of the key point data construction process of an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following is combined with Figure 1 -Attached Figure 2 , further details of this application are given.

[0026] In order to achieve precise control of the panel production process, existing technical means have certain defects. The manual detection method relies on manual operation, which is not only time-consuming and labor-intensive, but also prone to human errors and cannot achieve continuous and real-time monitoring. Secondly, the installation position of fixed sensors is limited, and it is difficult to cover all key links in the entire production process, resulting in some important parameters not being effectively monitored. In addition, the monitoring system based on wired communication has complex wiring, high maintenance costs, poor flexibility, and is not easy to adjust and expand. These problems limit the precise control and optimization of the panel printing production process. Therefore, the embodiment of the present application provides a panel printing monitoring method based on the Internet of Things, with reference to Figure 1 ,include: S1. Obtain a panel printing process, identify several key production links according to the panel printing process, and construct key point data according to the several key production links.

[0027] Specifically, different panel printing processes may exist depending on the model or type of panel required by different customers. Production line operators and managers should understand the relevant details of each production link in the panel printing production line, including production equipment, process flow, production efficiency, and common problems. Based on this understanding of the production process, they should determine which production links have the greatest impact on production efficiency and product quality, and select key production links based on this understanding.

[0028] S2. Obtain the current production environment and build production environment data based on the production environment.

[0029] Specifically, the production environment data includes at least temperature data and humidity data, and the production environment data can be collected through temperature sensors and humidity sensors to ensure compatibility with the existing production environment.

[0030] S3. Build the IoT architecture based on key point data and production environment data.

[0031] Specifically, the IoT architecture includes at least several different types of sensors, located at key production stages and appropriate for the current production environment. In implementation, it's necessary to select IoT sensors suitable for panel printing environments, such as ink level sensors, humidity sensors, and temperature sensors. These sensors must be installed and debugged to ensure accurate and stable data collection.

[0032] S4. Acquire real-time parameter data uploaded by the sensor module during the panel printing process.

[0033] Specifically, by integrating intelligent sensors, real-time monitoring of the panel printing process can be achieved, such as ink volume data monitored by the ink volume sensor, humidity data monitored by the humidity sensor, temperature data monitored by the temperature sensor, etc.

[0034] S5. Analyze the real-time parameter data and key point data to obtain monitoring results, and determine whether optimization is needed based on the monitoring results.

[0035] Specifically, a data analysis process is constructed, including data cleaning, preprocessing, storage and analysis, to achieve real-time monitoring of production data and obtain monitoring results through analysis.

[0036] S6. When optimization is required, an optimization plan is obtained based on real-time parameter data and key point data analysis, and the panel printing process is optimized through the optimization plan.

[0037] Specifically, the production process is intelligently optimized based on real-time parameter data and key point data, such as adjusting the ink volume, controlling humidity and temperature, etc., to improve printing quality and efficiency.

[0038] The implementation principle of the embodiment of the present application is: utilizing Industrial Internet of Things (IIoT) technology and intelligent manufacturing technology, monitoring the key links in the panel printing process by integrating different types of sensors, and automatically optimizing the production process based on the monitoring results, thereby achieving precise control and optimization of the panel production process.

[0039] In practice, IoT sensors include various types, such as temperature sensors, humidity sensors, and ink level sensors. These sensors can be selected from different brands and models based on actual needs. For example, temperature sensors can be PT100 or K-type thermocouples, humidity sensors can be resistive or capacitive, and ink level sensors can be photoelectric or magnetic. Each sensor has unique structural characteristics. For example, PT100 temperature sensors offer high accuracy and stability, making them suitable for high-temperature environments; while resistive humidity sensors offer low cost and fast response, making them suitable for rapidly changing humidity environments. IoT sensors connect to the data processing center via wireless communication. Common wireless communication methods include Wi-Fi, Bluetooth, or Zigbee. The appropriate wireless communication method depends on the application scenario and requirements. For example, Wi-Fi is suitable for long-distance transmission and large-scale data transmission, Bluetooth is suitable for short-distance, low-power transmission, and Zigbee is suitable for low-power transmission among large-scale network nodes. These wireless communication methods all offer good reliability and anti-interference capabilities, ensuring accurate data transmission. IoT sensors also have self-diagnostic capabilities, automatically sending fault information to the data processing center in the event of a sensor failure. This function is achieved through built-in self-test circuits and algorithms, which can promptly detect the working status of the sensor and reduce production interruptions caused by sensor failure.

[0040] like Figure 2 As shown, step S1 of the embodiment of the present application specifically includes: S11. Obtain the degree of influence of each production link in the panel printing process on production efficiency and product quality.

[0041] Specifically, the panel printing process usually includes: pre-treatment, cleaning and preparing the substrate to ensure that the surface is clean, free of oil and impurities, and to provide a good foundation for subsequent printing and adhesive application; photoresist coating, evenly coating the photoresist on the substrate to prepare for the subsequent exposure and development process; exposure, using a mask and a light source to transfer the designed pattern to the photoresist, and the part not blocked by the mask will be exposed; development, using a chemical solution to dissolve the unexposed photoresist, leaving the photoresist in the exposed area to form a pattern; etching, using a chemical solution or physical method to etch away the substrate material not protected by the photoresist to form a circuit or pattern; stripping, removing the remaining photoresist to ensure the substrate is The board surface is cleaned to prepare for subsequent processing; solder mask ink printing is to print solder mask ink in areas where soldering is not required to prevent solder from overflowing during soldering and affecting circuit performance; character printing is to print necessary characters and logos on the substrate through screen printing or other methods for easy identification and maintenance; surface treatment is to perform gold plating, silver plating, OSP (organic protective layer) and other surface treatments on the substrate to improve the conductivity and corrosion resistance of the circuit; shape cutting is to cut the substrate according to design requirements to form the final panel shape; quality inspection is to check the appearance and function of the finished product to ensure that it meets quality standards; packaging and shipment are to package the panels that have passed the quality inspection and prepare for shipment.

[0042] S12. Sort each production link of the panel printing process in descending order of the degree of impact on production efficiency and product quality.

[0043] Specifically, the degree of influence of each production link on production efficiency and product quality can be obtained through testing and inspection of actual production and experience of previous production processes.

[0044] S13. Select several production links ranked first as key production links.

[0045] Specifically, the top production links are determined as key production links based on the number of sensors to be installed, the accuracy requirements for panel printing monitoring, and the budget.

[0046] S14. Obtain key parameters corresponding to each key production link.

[0047] Specifically, the key parameters include at least ink volume, temperature, humidity and pressure.

[0048] S15. Construct several key production links and corresponding key parameters into key point data.

[0049] The implementation principle of the embodiment of the present application is: since the panel printing process has many production links, through the degree of influence of each production link on production efficiency and product quality, several production links with the greatest impact on production efficiency and product quality are selected as key production links for key monitoring, which can effectively improve the efficiency of panel printing monitoring and optimization.

[0050] Step S3 of the embodiment of the present application specifically includes: S31. Obtain IoT infrastructure based on key point data.

[0051] Specifically, the IoT infrastructure includes sensor types, data transmission methods, and data processing procedures. Designing the IoT architecture based on the production line layout and key monitoring points involves: Selecting sensors appropriate for the monitoring points, such as temperature, humidity, pressure, and ink level sensors. The data transmission method involves determining the protocol and method for data transmission, such as Wi-Fi, Bluetooth, Zigbee, or Ethernet, to ensure stability and security. The data processing and analysis process involves planning data preprocessing, storage, analysis, and visualization, including data cleaning, format conversion, storage solutions, and analysis algorithms.

[0052] S32. Determine the environmental compatibility of the IoT infrastructure based on production environment data.

[0053] Specifically, the compatibility judgment criteria include the adaptability with the interface, data format and communication protocol of existing equipment.

[0054] S33. Adjust the IoT infrastructure according to environmental compatibility to obtain an IoT architecture.

[0055] The implementation principle of the embodiment of the present application is: to build an Internet of Things architecture through key point data and production environment data, so that the final Internet of Things architecture can adapt to the current production process and production environment, thereby monitoring and optimizing the panel printing process according to actual conditions, and improving the intelligence and efficiency of panel printing.

[0056] Step S5 of the embodiment of the present application specifically includes: S51. Preset key parameter ranges based on key point data.

[0057] Specifically, for example, considering that the ink volume parameter in a certain key production link has a greater impact on product quality and production efficiency, the required ink volume parameter range is preset.

[0058] S52. Compare the real-time parameter data with the key parameter range to obtain monitoring results.

[0059] Specifically, the real-time ink volume data collected at the key production link is compared with the preset ink volume parameter range to obtain the current monitoring result.

[0060] The implementation principle of the embodiment of the present application is: by comparing the real-time parameter data with the preset range for monitoring, the current real-time parameter situation can be reflected in a timely manner, thereby improving the real-time and efficiency of panel printing monitoring.

[0061] Step S6 of the embodiment of the present application specifically includes: S61. Construct an initial model for optimizing the prediction scheme.

[0062] Specifically, an optimization scheme is constructed based on LSTM to detect an initial model, and model parameters of the optimization scheme are configured to detect the initial model. The model parameters include but are not limited to the number of LSTM units, activation function, and input sequence length.

[0063] S62, obtaining real-time parameter data samples, key point data samples and corresponding optimization solution samples during several panel printing optimization processes in the optimization solution database, S63. Use the real-time parameter data samples and key point data samples as input features, and use the optimization solution samples as output features to construct a sample set.

[0064] Specifically, the optimization solution database stores real-time parameter data samples, key point data samples and corresponding optimization solution samples acquired and stored during previous optimizations. The real-time parameter data samples and key point data samples are used as input, and the optimization solution samples are used as output to construct a sample set.

[0065] S64. Train the initial optimization solution prediction model based on the sample set to obtain a trained optimization solution prediction model.

[0066] Specifically, a sample set is used to train the initial model for optimization scheme detection, in which real-time parameter data samples and key point data samples are used as input data, and optimization scheme samples are used as output data, so that the model can learn the feature relationship between real-time parameter data samples, key point data samples and optimization scheme samples.

[0067] S65. Input the real-time parameter data and key point data into the trained optimization solution prediction model to obtain an optimization solution.

[0068] Specifically, the optimization solution detection model predicts the corresponding optimization solution detection result based on the learned feature relationship and the input optimization solution detection data set, and outputs the optimization solution detection result.

[0069] The implementation principle of the embodiment of the present application is: by constructing and training an optimization scheme prediction model, the model can autonomously learn the characteristic relationship between real-time parameter data, key point data and optimization scheme based on big data, ensure the accuracy of the final prediction, and improve the intelligence and efficiency of optimization scheme generation.

[0070] This embodiment of the application also includes building a feedback mechanism, specifically including: feeding back monitoring results to a management terminal or remote terminal; obtaining feedback result data, and adjusting the optimization plan based on the feedback result data. In the specific implementation process, the management terminal can be a management device in the central control room or a mobile device of the management personnel, so that the management personnel can monitor the panel printing process and adjust the optimization plan at any time.

[0071] The implementation principle of the embodiment of the present application is: by building a feedback mechanism, the monitoring results and optimization solutions are fed back to production management personnel so as to adjust the production strategy in a timely manner, continuously optimize performance, and improve monitoring accuracy and production efficiency.

[0072] This embodiment of the present application also includes building a visualization mechanism, specifically including: generating real-time parameter data trend charts and early warning prompts based on monitoring results; generating visualization signals based on the parameter data trend charts, early warning prompts, and optimization solutions, and sending the visualization signals to a visualization interface. In specific implementations, the visualization interface can be located on a management device in the central control room or on a mobile device used by a manager, allowing the manager to monitor the panel printing process and adjust optimization solutions at any time.

[0073] The implementation principle of the embodiment of the present application is: by constructing a visualization mechanism to dim and visualize the monitoring results and optimization plans, a real-time monitoring function is provided to facilitate production management personnel to quickly understand and adjust the production status.

[0074] The present application also provides a computer device including a memory and a processor. The memory stores a computer program capable of being loaded and executed by the processor. The computer program includes: S1. Obtain a panel printing process, identify several key production links according to the panel printing process, and construct key point data according to the several key production links.

[0075] S2. Obtain the current production environment and build production environment data based on the production environment.

[0076] S3. Build the IoT architecture based on key point data and production environment data.

[0077] S4. Acquire real-time parameter data uploaded by the sensor module during the panel printing process.

[0078] S5. Analyze the real-time parameter data and key point data to obtain monitoring results, and determine whether optimization is needed based on the monitoring results.

[0079] S6. When optimization is required, an optimization plan is obtained based on real-time parameter data and key point data analysis, and the panel printing process is optimized through the optimization plan.

[0080] The computer is a server, and the computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store panel printing processes, key point data, production environment data, Internet of Things architecture, etc. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a panel printing monitoring method based on the Internet of Things can be implemented.

[0081] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented: S1. Obtain a panel printing process, identify several key production links according to the panel printing process, and construct key point data according to the several key production links.

[0082] S2. Obtain the current production environment and build production environment data based on the production environment.

[0083] S3. Build the IoT architecture based on key point data and production environment data.

[0084] S4. Acquire real-time parameter data uploaded by the sensor module during the panel printing process.

[0085] S5. Analyze the real-time parameter data and key point data to obtain monitoring results, and determine whether optimization is needed based on the monitoring results.

[0086] S6. When optimization is required, an optimization plan is obtained based on real-time parameter data and key point data analysis, and the panel printing process is optimized through the optimization plan.

[0087] In addition, the processor in the computer device executes all the steps of the above-mentioned panel printing monitoring method based on the Internet of Things when executing the computer program.

[0088] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0089] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0090] The examples of this specific embodiment are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, any equivalent changes made based on the structure, shape, and principle of this application should be included in the scope of protection of this application.

Claims

1. A panel printing monitoring method based on the Internet of Things, characterized in that: include: Obtain the panel printing process, identify several key production links according to the panel printing process, and construct key point data according to several key production links; Acquire the current production environment, and construct production environment data according to the production environment, wherein the production environment data at least includes temperature data and humidity data; Building an Internet of Things architecture based on key point data and production environment data, wherein the Internet of Things architecture includes at least a number of different types of sensors respectively located at a number of key production links and applicable to the current production environment; Obtain real-time parameter data uploaded by the sensor module during the panel printing process; Get monitoring results based on real-time parameter data and key point data analysis, and determine whether optimization is needed based on the monitoring results; When optimization is needed, an optimization plan is obtained based on real-time parameter data and key point data analysis, and the panel printing process is optimized through the optimization plan.

2. The panel printing detection method based on the Internet of Things according to claim 1 is characterized in that: The panel printing process is obtained, and several key production links are identified according to the panel printing process, specifically including: Obtain the impact of each production link of the panel printing process on production efficiency and product quality; Rank each production link of the panel printing process in descending order of impact on production efficiency and product quality; Select several production links that are ranked first as key production links.

3. The panel printing monitoring method based on the Internet of Things according to claim 2 is characterized in that: The key point data is constructed according to several key production links, specifically including: Obtain key parameters corresponding to each key production link, wherein the key parameters at least include ink volume, temperature, humidity and pressure; Several key production links and corresponding key parameters are constructed as key point data.

4. The panel printing monitoring method based on the Internet of Things according to claim 3 is characterized in that: The IoT architecture is constructed based on key point data and production environment data, specifically including: Acquire an IoT infrastructure based on key point data, wherein the IoT infrastructure includes sensor types, data transmission methods, and data processing procedures; Determine the environmental compatibility of the IoT infrastructure based on production environment data, where the compatibility criteria include compatibility with the interface, data format, and communication protocol of existing devices; The IoT infrastructure is adjusted according to environmental compatibility to obtain the IoT architecture.

5. The panel printing monitoring method based on the Internet of Things according to claim 4 is characterized in that: The monitoring results obtained by analyzing the real-time parameter data and key point data specifically include: Preset key parameter ranges based on key point data; Compare the real-time parameter data with the key parameter range to obtain the monitoring results.

6. The panel printing monitoring method based on the Internet of Things according to claim 5 is characterized in that: The optimization scheme obtained by analyzing the real-time parameter data and key point data specifically includes: Construct an initial model for predicting optimization solutions; Acquire real-time parameter data samples, key point data samples and corresponding optimization solution samples in several panel printing optimization processes in the optimization solution database; The real-time parameter data samples and key point data samples are used as input features, and the optimization solution samples are used as output features to construct a sample set; The initial model for predicting the optimization scheme is trained according to the sample set to obtain a trained prediction model for the optimization scheme; The real-time parameter data and key point data are input into the trained optimization solution prediction model to obtain the optimization solution.

7. A panel printing monitoring method based on the Internet of Things according to any one of claims 1 to 6, characterized in that: It also includes building a feedback mechanism, including: Feedback the monitoring results to the management end or remote end; Obtain feedback result data and adjust the optimization plan based on the feedback result data.

8. A panel printing monitoring method based on the Internet of Things according to any one of claims 1 to 6, characterized in that: It also includes building visualization mechanisms, including: Generate real-time parameter data change trend charts and early warning prompts based on monitoring results; Generate visualization signals based on parameter data change trend charts, early warning prompts and optimization plans, and send the visualization signals to the visualization interface.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a panel printing monitoring method based on the Internet of Things that can be loaded and executed by the processor as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and executes a panel printing monitoring method based on the Internet of Things as described in any one of claims 1 to 8.

Images