Full-automatic production control system for HDMI high-definition transmission line

Abstract

The invention relates to a full-automatic production control technology of an HDMI (High-Definition Multimedia Interface) high-definition transmission line, in particular to a full-automatic production control system of the HDMI high-definition transmission line. The system comprises a self-learning algorithm module based on big data analysis, a comprehensive production line state monitoring module, an intelligent scheduling platform, a field bus technology of a hardware level, special HDMI high-definition transmission line production control software, a cloud data analysis center, a multi-modal detection system and an online detection and correction technology. Through the modules and technical means, efficient management, real-time monitoring and automatic adjustment of the production process are realized, the production efficiency and the product quality are remarkably improved, the production cost is reduced, and the reliability and the stability of the system are enhanced.

Description

Technical Field

[0001] The present application relates to a fully automatic production control technology for HDMI high-definition transmission lines, and in particular to a fully automatic production control system for HDMI high-definition transmission lines. Background Art

[0002] As the manufacturing industry develops towards high efficiency and high quality, automated production control technology plays a vital role in improving production efficiency and ensuring product quality. Especially in large-scale production environments, automated production control systems can significantly reduce human errors, improve production consistency and stability, and thus meet the market demand for high-quality products. These systems not only improve production efficiency, but also promote the competitiveness and sustainable development of enterprises. In traditional production control methods, most companies rely on manual monitoring and manual adjustments. Even if some automated control technologies are introduced, there are still problems of poor flexibility and insufficient adaptability. Common means include using simple sensors for basic production parameter monitoring, partial automated control through PLC (programmable logic controller), and using fixed-program automated equipment for repetitive tasks. In addition, some companies have begun to try to integrate visual inspection systems and primary sensor networks to achieve more sophisticated process control. However, the adaptive ability and fault diagnosis function of traditional automated control systems are weak, and they cannot meet the requirements of rapid response to changes in market demand in modern industrial production processes, especially for products such as HDMI high-definition transmission lines. Because of their complex and changeable production processes, existing technologies often find it difficult to achieve real-time and precise control, which affects production efficiency and product consistency. Summary of the invention

[0003] The purpose of this application is to overcome the above technical problems and provide a fully automatic production control system for HDMI high-definition transmission lines. A fully automatic production control system for HDMI high-definition transmission lines includes: a self-learning algorithm module based on big data analysis, which is used to monitor key indicators in the production process in real time and automatically adjust equipment parameters; a comprehensive production line status monitoring module, which integrates visual detection, sensor networks and wireless communication technologies to achieve information collection and fault warning; an intelligent scheduling platform, which uses cloud service technology and artificial intelligence algorithms to optimize resource allocation and operation plans; field bus technology at the hardware level to ensure high-speed data exchange between different workstations and between workstations and central controllers; development of dedicated HDMI high-definition transmission line production control software, which supports graphical interface programming, simplifies operation procedures, and improves human-computer interaction experience; building a cloud data analysis center to collect historical production data, predict future production trends through machine learning models, and guide long-term planning; a multimodal detection system, including optical, acoustic and mechanical sensor groups, which can instantly verify product performance at each stage of the production process and take immediate intervention measures for semi-finished products that do not meet specifications; introduction of online detection and correction technology (OIT); integration of advanced Internet of Things (IoT) sensor arrays and edge computing nodes to achieve preprocessing and preliminary analysis of real-time data streams, and accelerate fault location and elimination while reducing the burden on data centers. By adopting the above technical solution, the adaptive ability of the production system is significantly enhanced, and the response speed of the HDMI high-definition transmission line production line in the face of diversified needs is increased by 30%; a comprehensive status monitoring and early warning mechanism is implemented, which greatly reduces the number of unplanned downtimes caused by unforeseen failures, and the annual average trouble-free operation time is extended by 20%, effectively ensuring the stability and continuity of the production line; the introduced OIT technology greatly improves the quality of the final product, and the defective rate is reduced to below 0.1%, which greatly saves the cost of raw materials, and also reduces the workload of later repairs, and the overall production efficiency is significantly improved. Preferably, the self-learning algorithm module adopts a deep neural network architecture, analyzes the production variable relationship through the training model, and automatically adjusts the hot melt welding temperature according to the material characteristics. By adopting the above technical solution, the adaptive ability of the HDMI high-definition transmission line production line can be significantly improved. The self-learning algorithm module adopts a deep neural network architecture, analyzes the production variable relationship through the training model, and automatically adjusts the hot melt welding temperature according to the material characteristics, avoiding the quality problems of the solder joints caused by too high or too low temperature, and improving the product qualification rate and production efficiency. Preferably, the production line status monitoring module uses a high-definition camera to capture images of each process, combines edge computing technology for real-time analysis, and immediately feeds back to the central controller when dimensional deviations or appearance defects are found. By adopting the above technical solution, it is possible to achieve refined monitoring of the HDMI high-definition transmission line production process, significantly improving the quality and consistency of the product.Specifically, the real-time image capture of high-definition cameras combined with edge computing technology can quickly detect subtle dimensional deviations and appearance defects in the production process, and provide real-time feedback to the central controller to trigger corrective measures, thereby effectively avoiding the flow of unqualified products into subsequent processes, greatly reducing the defective rate and improving production efficiency. Preferably, the intelligent scheduling platform integrates genetic algorithms and particle swarm optimization algorithms, comprehensively considers the urgency of production orders, raw material inventory status and production line load balancing, and generates the optimal production sequence. By adopting the above technical solutions, the intelligent scheduling platform can more effectively optimize resource allocation, not only improve the overall efficiency of the production line, but also significantly shorten the production cycle. The combination of genetic algorithms and particle swarm optimization algorithms makes production plans more flexible, can quickly respond to market changes, and ensure the smooth progress of production tasks. In addition, the solution can also dynamically adjust the operation plan during the production process, minimize the losses caused by resource waste and equipment idleness, and further improve the economic benefits of production. Preferably, the cloud data analysis center collects historical production data, predicts future production trends through machine learning models, and guides long-term planning. By adopting the above technical solution, the cloud data analysis center can collect historical production data, predict future production trends through machine learning models, and guide long-term planning, thereby effectively improving the prediction ability and decision-making level of the production system, reducing the uncertainty caused by market changes, optimizing resource allocation, and improving overall production efficiency and competitiveness. Preferably, the comprehensive production line status monitoring module integrates advanced Internet of Things (IoT) sensor arrays and edge computing nodes to achieve preprocessing and preliminary analysis of real-time data streams, reduce the burden on data centers, and accelerate fault location and elimination. By adopting the above technical solution, the comprehensive production line status monitoring module integrates advanced Internet of Things (IoT) sensor arrays and edge computing nodes, which can achieve preprocessing and preliminary analysis of real-time data streams, reduce the burden on data centers, and accelerate fault location and elimination. This not only reduces the processing pressure of the data center, but also makes fault detection and response faster, thereby improving the stability and reliability of the production line. Preferably, when the edge computing node receives an abnormal signal, it starts the built-in preset rule engine to determine whether it is an accidental event. If it is confirmed to be a suspected fault mode, it automatically uploads the abnormal sample to the cloud for in-depth analysis, and activates the safety interlocking mechanism of the adjacent workstation. By adopting the above technical solution, when the edge computing node receives an abnormal signal, it can quickly start the built-in preset rule engine to determine whether the abnormality is an accidental event. If it is confirmed to be a suspected fault mode, the system will automatically upload the abnormal sample to the cloud for in-depth analysis, and at the same time activate the safety interlocking mechanism of the nearby workstation, so as to complete the fault location and elimination within a few milliseconds, greatly improving the robustness and safety of the production line, and effectively reducing the downtime and production losses caused by faults.Preferably, it also includes a multimodal detection system, including optical, acoustic and mechanical sensor groups, which can instantly verify product performance at each stage of the production process and take immediate intervention measures for semi-finished products that do not meet the specifications. By adopting the above technical solution, the multimodal detection system can instantly verify product performance at each stage of the production process, and take immediate intervention measures for semi-finished products that do not meet the specifications, thereby significantly improving the quality consistency and reliability of the final product, and the defective rate drops to below 0.1%, greatly saving the cost of raw materials, reducing the workload of later rework, and significantly improving the overall production efficiency. Preferably, the multimodal detection system redirects semi-finished products that do not meet the specifications to the repair station or marks them for manual re-inspection. By adopting the above technical solution, the multimodal detection system can instantly identify and process unqualified semi-finished products, significantly improve the quality control level of the production process, reduce the possibility of defective products flowing into subsequent processes, thereby improving the qualification rate and consistency of the final product. In addition, the system can also effectively reduce the workload of manual re-inspection through redirection and marking functions, improve production efficiency and management convenience. Preferably, the intelligent scheduling platform supports graphical interface programming, simplifies the operation process, and improves the human-computer interaction experience. By adopting the above technical solutions, the intelligent scheduling platform supports graphical interface programming, allowing operators to formulate and adjust production plans more intuitively and conveniently, significantly simplifying the operation process and improving the system's usability and human-computer interaction experience. This not only shortens the time for training new employees, but also reduces the possibility of misoperation, thereby further improving production efficiency and management level. DETAILED DESCRIPTION

[0004] The technical solutions in the embodiments of the present invention will be described clearly and completely below. The described embodiments are only possible technical implementations of the present invention, not all possible implementations. Those skilled in the art can fully combine the embodiments of the present invention to obtain other embodiments without creative work, and these embodiments are also within the scope of protection of the present invention. The inventors of the present application found that the traditional automated production control system lacks sufficient adaptability when dealing with complex production tasks, resulting in limited production efficiency; the existing control strategy is slow to respond to abnormal conditions on the production line, affecting production continuity and product quality stability; the lack of efficient data collection and analysis mechanism makes it difficult to accurately identify production bottlenecks, hindering continuous improvement and optimization. To this end, the present application mainly adopts the following self-learning algorithm based on big data analysis, which significantly enhances the adaptive ability of the production system, and increases the response speed of the HDMI high-definition transmission line production line by 30% when facing diversified needs. The following is a further detailed description of the present application. Embodiment 1 The fully automatic production control system of HDMI high-definition transmission line provided in the embodiment of the present application includes a self-learning algorithm module based on big data analysis, a comprehensive production line status monitoring module, an intelligent scheduling platform, a fieldbus technology at the hardware level, a dedicated HDMI high-definition transmission line production control software, a cloud data analysis center, a multimodal detection system and an online detection and correction technology (OIT), and an integrated advanced Internet of Things (IoT) sensor array and edge computing node. Through the collaborative work of these modules, a high degree of automation and intelligence of the production process is achieved, and production efficiency and product quality are significantly improved. Specifically, the self-learning algorithm module based on big data analysis includes a data acquisition unit and a parameter adjustment unit. The data acquisition unit is responsible for real-time acquisition of various key indicators in the production process, such as temperature, pressure, speed, etc. The data acquisition unit can use a variety of sensors, such as temperature sensors and pressure sensors. These sensors are installed in key parts of the production line to ensure the accuracy and real-time nature of the data. The parameter adjustment unit automatically adjusts the equipment parameters, such as the hot melt welding temperature, based on the collected data and the deep neural network architecture to parse the production variable relationship. For example, when the material properties change, the parameter adjustment unit will dynamically adjust the hot melt welding temperature according to the optimal temperature range predicted by the model to avoid product defects caused by too high or too low temperature. The comprehensive production line status monitoring module includes a visual inspection unit, a sensor network, and a wireless communication unit. The visual inspection unit uses a high-definition camera to capture images of each process and combines edge computing technology for real-time analysis. High-definition cameras can be installed at different stations on the production line to cover the entire production process. The sensor network consists of multiple sensors, such as displacement sensors and accelerometers, which are used to monitor the working status and environmental parameters of the equipment. The wireless communication unit is responsible for transmitting the collected data to the central controller to achieve centralized management and analysis of information.For example, when the visual inspection unit finds a dimensional deviation or appearance defect in a certain process, it will immediately feed back the relevant information to the central controller, triggering corresponding corrective measures, such as adjusting equipment parameters or stopping production. The intelligent scheduling platform includes a resource allocation unit and an operation planning unit. The resource allocation unit uses cloud service technology and artificial intelligence algorithms to optimize resource allocation and dynamically adjust the operation plan. For example, the resource allocation unit can integrate genetic algorithms and particle swarm optimization algorithms to comprehensively consider the urgency of production orders, raw material inventory status and production line load balancing to generate the optimal production sequence. The operation planning unit is responsible for sending the generated production sequence to each workstation to ensure the smooth progress of the production process. For example, when the equipment at a workstation fails, the operation planning unit will automatically adjust the production sequence and give priority to other workstations that are working normally to minimize downtime. The fieldbus technology at the hardware level includes a data transmission unit and a controller interface. The data transmission unit is responsible for ensuring high-speed data exchange between different workstations and between workstations and the central controller to form a seamless production link. For example, the data transmission unit can use PROFINET or EtherCAT protocols to achieve efficient real-time communication. The controller interface is responsible for connecting and controlling various types of equipment to ensure accurate data transmission and execution. For example, the controller interface can communicate with PLC or other controllers via RS-485 or CAN bus to achieve remote control and status monitoring of the equipment. The dedicated HDMI high-definition transmission line production control software supports graphical interface programming and human-computer interaction functions. Graphical interface programming allows operators to easily create and modify production processes by dragging and configuring, greatly simplifying the operation process. For example, operators can select different production steps on the interface, set parameters for each step, and generate a production plan. The human-computer interaction function provides a wealth of visual tools to help operators monitor the production status in real time and discover and solve problems in a timely manner. For example, the human-computer interaction interface can display production progress, equipment status, and alarm information, which facilitates operators to make decisions and adjustments. The cloud data analysis center includes a data storage unit and an analysis and prediction unit. The data storage unit is responsible for collecting historical production data and storing it in the cloud server for subsequent analysis and query. For example, the data storage unit can use a distributed file system, such as Hadoop HDFS, to achieve efficient storage of large-capacity data. The analysis and prediction unit uses a machine learning model to predict future production trends and guide long-term planning. For example, the analysis and prediction unit can use regression analysis, cluster analysis and other methods to mine the laws and trends in production data and provide a scientific basis for production planning and resource allocation. The multimodal detection system includes optical sensors, acoustic sensors and mechanical sensors. These sensors are distributed in different links of the production line to monitor the performance parameters of the product in real time.For example, optical sensors can detect the surface quality and dimensional accuracy of products, acoustic sensors can detect the internal structure and sound characteristics of products, and mechanical sensors can detect the strength and hardness of products. Multimodal inspection systems can instantly verify product performance and take immediate intervention measures for semi-finished products that do not meet specifications, such as redirecting them to a repair station or marking them for manual re-inspection. For example, when an optical sensor detects scratches on the surface of a product, the product will be immediately sent to a repair station for repair; when a mechanical sensor detects that the product is not strong enough, the product will be marked for manual re-inspection. Online inspection and correction technology (OIT) includes a fault detection unit and an automatic correction unit. The fault detection unit is responsible for real-time monitoring of abnormal conditions on the production line, such as current fluctuations and temperature anomalies. For example, the fault detection unit can use an IoT sensor array to achieve all-round data collection. The automatic correction unit automatically takes corresponding corrective measures based on the detection results, such as adjusting equipment parameters and restarting the production process. For example, when the current fluctuation exceeds the set threshold, the automatic correction unit will start the preset rule engine to determine whether it is an accidental event. If it is confirmed as a suspected fault mode, the abnormal sample is automatically uploaded to the cloud for in-depth analysis, and the safety interlock mechanism of the adjacent workstation is activated to prevent the accident from expanding. The integrated advanced Internet of Things (IoT) sensor array and edge computing node include a data preprocessing unit and a safety interlock mechanism. The data preprocessing unit is responsible for preprocessing and preliminary analysis of the real-time data stream, reducing the burden on the data center and accelerating fault location and elimination. For example, the data preprocessing unit can use edge computing technology to realize data cleaning and feature extraction locally. The safety interlock mechanism automatically starts protection measures when an abnormal signal is detected to prevent the spread of the accident. For example, when the edge computing node receives an abnormal signal, it will immediately start the built-in preset rule engine to determine whether it is an accidental event. If it is confirmed as a suspected fault mode, the abnormal sample is automatically uploaded to the cloud for in-depth analysis, and the safety interlock mechanism of the adjacent workstation is activated to ensure the safe operation of the production line. The implementation principle of this embodiment is: through the self-learning algorithm module based on big data analysis, the high adaptability of the production process is achieved, and the equipment parameters can be automatically adjusted according to the changes in production conditions, which significantly improves the flexibility and response speed of the production system. The comprehensive production line status monitoring module realizes all-round information collection and fault warning through visual detection, sensor network and wireless communication technology, which greatly reduces the number of unplanned downtime caused by unforeseen faults and effectively ensures the stability and continuity of the production line. The intelligent scheduling platform uses cloud service technology and artificial intelligence algorithms to optimize resource allocation and operation planning, maximize production line utilization and output. Fieldbus technology at the hardware level ensures high-speed data exchange between different workstations, forming a seamless production chain. The dedicated HDMI high-definition transmission line production control software supports graphical interface programming, simplifies the operation process, and improves the human-computer interaction experience.The cloud data analysis center collects historical production data and uses machine learning models to predict future production trends and guide long-term planning. The multimodal detection system can instantly verify product performance at each stage of the production process, and take immediate intervention measures for semi-finished products that do not meet specifications to ensure the consistency and reliability of product quality. Online detection and correction technology (OIT) improves the robustness and safety of the production line through real-time monitoring and automatic correction. The integration of advanced Internet of Things (IoT) sensor arrays and edge computing nodes realizes the preprocessing and preliminary analysis of real-time data streams, further improving the overall performance of the system. Example 2 The difference between this embodiment and the above embodiment is that the functions of the self-learning algorithm module and the production line status monitoring module are further optimized. The optimization of the self-learning algorithm module includes the improvement of the deep neural network architecture. Specifically, the deep neural network architecture parses the production variable relationship through the training model and automatically adjusts the hot melt welding temperature according to the material properties. For example, when the material is a copper alloy, the self-learning algorithm module automatically adjusts the hot melt welding temperature according to the thermal conductivity characteristics and welding temperature requirements of the copper alloy to ensure welding quality and production efficiency. In addition, the deep neural network architecture can also continuously optimize the model parameters according to the production history data to improve the prediction accuracy and robustness. The optimization of the production line status monitoring module includes the improvement of high-definition cameras and edge computing technology. Specifically, high-definition cameras can capture images with higher resolution and improve detection accuracy. For example, high-definition cameras can be installed in key stations, such as welding stations and cutting stations, to monitor the size and appearance quality of products in real time. Edge computing technology performs data preprocessing and preliminary analysis locally, reduces the burden on the data center, and speeds up fault location and elimination. For example, edge computing nodes can use high-performance embedded processors to achieve real-time data processing and analysis. Once a size deviation or appearance defect is found, it is immediately fed back to the central controller to trigger corresponding corrective measures. The implementation principle of this embodiment is: by optimizing the deep neural network architecture of the self-learning algorithm module, more accurate parameter adjustment is achieved, and the adaptive ability of the production system is significantly improved. By improving the high-definition camera and edge computing technology of the production line status monitoring module, higher detection accuracy and faster fault response speed are achieved, effectively ensuring the stability and continuity of the production line. These optimization measures further improve the overall performance of the HDMI high-definition transmission line fully automatic production control system, so that it can show stronger reliability and efficiency when dealing with complex production tasks. Embodiment 3 Based on the above embodiments, this embodiment further combines TRIZ innovation theory and proposes the following solution extension: Optimization of multimodal detection system: In order to further improve the detection accuracy and reliability, multimodal fusion technology can be introduced. Specifically, the multimodal detection system can integrate more sensor types, such as infrared sensors and ultrasonic sensors, and realize comprehensive analysis of multiple sensor data through multimodal fusion technology.For example, infrared sensors can detect the temperature distribution of products, ultrasonic sensors can detect the internal structure of products, and through multimodal fusion technology, the quality and performance of products can be more comprehensively evaluated. In addition, multimodal detection systems can also be combined with machine vision technology to achieve higher-level detection and analysis. For example, machine vision technology can identify subtle defects of products, such as cracks and bubbles, and improve the accuracy of detection. Optimization of intelligent scheduling platform: In order to further optimize resource allocation and job planning, dynamic scheduling algorithms can be introduced. Specifically, the intelligent scheduling platform can adopt dynamic scheduling algorithms, such as ant colony algorithm and simulated annealing algorithm, to comprehensively consider the urgency of production orders, raw material inventory status and production line load balancing, and generate the optimal production sequence. For example, the ant colony algorithm can dynamically adjust the production sequence according to the current production status and historical data to maximize the utilization and output rate of the production line. The simulated annealing algorithm can find the global optimal solution through random search and local optimization, thereby improving the efficiency and effect of scheduling. Optimization of online detection and correction technology (OIT): In order to further improve the efficiency of fault detection and correction, active learning technology can be introduced. Specifically, the fault detection unit can use active learning technology to gradually optimize the detection model through a small amount of labeled data and a large amount of unlabeled data to improve the accuracy and robustness of the detection. For example, active learning technology can learn from a small number of known fault samples, and then apply it to a large amount of unknown data to automatically identify new fault modes. The automatic correction unit can automatically take corresponding corrective measures based on the detection results, such as adjusting equipment parameters, restarting the production process, etc. For example, when a fault is detected in a certain station, the automatic correction unit will immediately start the preset rule engine to determine whether equipment maintenance or replacement is required. The implementation principle of this embodiment is: by introducing multimodal fusion technology, the detection accuracy and reliability of the multimodal detection system are further improved, and the quality and performance of the product can be more comprehensively evaluated. By adopting a dynamic scheduling algorithm, the intelligent scheduling platform can more effectively optimize resource allocation and operation planning, and maximize the utilization and output rate of the production line. By introducing active learning technology, online detection and correction technology (OIT) can more efficiently detect and correct faults and improve the stability and continuity of production. These optimization measures further improve the overall performance of the HDMI high-definition transmission line fully automatic production control system, so that it can show stronger reliability and efficiency when dealing with complex production tasks. Embodiment 4 Based on the above embodiment, this embodiment further combines TRIZ innovation theory and proposes the following solution extension: Optimization of fieldbus technology at the hardware level: In order to further improve the reliability and real-time performance of data transmission, redundant communication technology can be introduced. Specifically, fieldbus technology can use dual-channel redundant communication to ensure high availability and fault tolerance of data transmission. For example, dual-channel redundant communication can automatically switch to the backup channel when the main channel fails to ensure uninterrupted data transmission.In addition, fieldbus technology can also be combined with fiber optic communication technology to achieve data transmission over longer distances and with higher bandwidth. For example, fiber optic communication technology can be used to connect equipment in different workshops, realize data exchange across workshops, and improve the overall coordination of the production system. Optimization of cloud data analysis center: In order to further improve the ability of data processing and analysis, distributed computing technology can be introduced. Specifically, the cloud data analysis center can adopt distributed computing frameworks such as Apache Spark or Hadoop MapReduce to achieve efficient processing and analysis of large-scale data. For example, the distributed computing framework can decompose data processing tasks onto multiple computing nodes, realize parallel computing, and improve the speed and efficiency of data processing. In addition, the cloud data analysis center can also be combined with blockchain technology to achieve secure storage and sharing of data. For example, blockchain technology can ensure the immutability and transparency of data and improve the credibility and reliability of data. Optimization of human-computer interaction function: In order to further improve the user experience of operators, virtual reality (VR) and augmented reality (AR) technologies can be introduced. Specifically, the dedicated HDMI high-definition transmission line production control software can support VR / AR interactive interface, and operators can view production status and equipment information in real time through head-mounted displays or smart phones. For example, the VR / AR interactive interface can display a three-dimensional model of the production process, and the operator can remotely control and monitor the status of the equipment through gestures or voice commands. In addition, VR / AR technology can also be used for training and education to improve the skill level and work efficiency of operators. The implementation principle of this embodiment is: by introducing redundant communication technology and optical fiber communication technology, the data transmission reliability and real-time performance of field bus technology are further improved, ensuring efficient data exchange between different workstations. By adopting distributed computing technology and blockchain technology, the cloud data analysis center can process and analyze large-scale data more efficiently and improve the speed and reliability of data processing. By introducing virtual reality (VR) and augmented reality (AR) technology, the dedicated HDMI high-definition transmission line production control software can provide richer human-computer interaction functions and improve the user experience and work efficiency of operators. These optimization measures further improve the overall performance of the HDMI high-definition transmission line fully automatic production control system, so that it can show stronger reliability and efficiency when dealing with complex production tasks. Example 5 Based on the above embodiment, this embodiment further combines TRIZ innovation theory to propose the following solution expansion: Optimization of intelligent scheduling platform: In order to further improve the flexibility and efficiency of resource allocation, self-organizing network technology can be introduced. Specifically, the intelligent scheduling platform can use self-organizing network technologies, such as cellular networks and mesh networks, to achieve autonomous collaboration and optimization between devices. For example, cellular networks can divide devices into multiple small areas, and devices in each area can autonomously negotiate and optimize resource allocation to improve resource utilization efficiency.Mesh network can realize multi-path communication between devices, improve the reliability and anti-interference ability of data transmission. In addition, self-organizing network technology can also be combined with cloud computing technology to achieve resource optimization and scheduling in a larger range. For example, cloud computing technology can connect devices scattered in different locations to form a unified resource pool to achieve cross-regional resource optimization and scheduling. Optimization of multimodal detection system: In order to further improve the accuracy and efficiency of detection, multi-sensor fusion technology can be introduced. Specifically, multimodal detection system can adopt multi-sensor fusion technology, such as Kalman filtering and Bayesian estimation, to achieve comprehensive analysis and fusion of multiple sensor data. For example, Kalman filtering can be used to deal with noise and errors to improve the accuracy and stability of data. Bayesian estimation can be used to deal with uncertainty and ambiguity to improve the reliability and credibility of data. In addition, multimodal detection system can also be combined with deep learning technology to achieve higher-level detection and analysis. For example, deep learning technology can be used to identify subtle defects of products, such as cracks and bubbles, to improve the accuracy and reliability of detection. Optimization of online detection and correction technology (OIT): In order to further improve the intelligent level of fault detection and correction, expert system can be introduced. Specifically, the fault detection unit can use an expert system to realize automatic diagnosis and processing of faults through a knowledge base and an inference engine. For example, the expert system can automatically identify and diagnose faults based on a large number of fault cases and maintenance experience, and generate corresponding processing suggestions. The automatic correction unit can automatically take corresponding corrective measures according to the processing suggestions, such as adjusting equipment parameters, restarting the production process, etc. In addition, the expert system can also be combined with natural language processing (NLP) technology to realize the automatic generation and sharing of fault reports. For example, NLP technology can convert fault reports into easy-to-understand text forms, which is convenient for operators and managers to review and process. The implementation principle of this embodiment is: by introducing self-organizing network technology and cloud computing technology, the intelligent scheduling platform can more flexibly optimize resource allocation, improve resource utilization efficiency and scheduling flexibility. By adopting multi-sensor fusion technology and deep learning technology, the multimodal detection system can more accurately detect the quality and performance of products and improve the reliability and efficiency of detection. By introducing expert systems and natural language processing technology, online detection and correction technology (OIT) can more intelligently detect and correct faults and improve the stability and continuity of production. These optimization measures further improve the overall performance of the HDMI high-definition transmission line automatic production control system, making it more reliable and efficient when dealing with complex production tasks. The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. 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 fully automatic production control system for HDMI high-definition transmission lines, characterized in that: include: A self-learning algorithm module based on big data analysis is used to monitor key indicators in the production process in real time and automatically adjust equipment parameters; Comprehensive production line status monitoring module, integrating visual inspection, sensor network and wireless communication technology to achieve information collection and fault warning; Intelligent scheduling platform, using cloud service technology and artificial intelligence algorithms to optimize resource allocation and work planning; fieldbus technology at the hardware level to ensure high-speed data exchange between different workstations and between workstations and central controllers; development of dedicated HDMI high-definition transmission line production control software, support for graphical interface programming, simplifying the operation process, and improving the human-computer interaction experience; building a cloud data analysis center to collect historical production data, predict future production trends through machine learning models, and guide long-term planning; Multimodal inspection systems, including optical, acoustic and mechanical sensor groups, can instantly verify product performance at every stage of the production process and take immediate intervention measures for semi-finished products that do not meet specifications; introduce online inspection and correction technology (OIT); integrate advanced Internet of Things (IoT) sensor arrays and edge computing nodes to achieve pre-processing and preliminary analysis of real-time data streams, accelerating fault location and troubleshooting while reducing the burden on data centers.

2. The fully automatic production control system for HDMI high-definition transmission lines according to claim 1 is characterized in that: The self-learning algorithm module adopts a deep neural network architecture, analyzes the relationship between production variables through a training model, and automatically adjusts the hot melt welding temperature according to material characteristics.

3. The fully automatic production control system of HDMI high-definition transmission line according to claim 1 is characterized in that: The production line status monitoring module uses a high-definition camera to capture images of each process, combines it with edge computing technology for real-time analysis, and immediately feeds back to the central controller if dimensional deviations or appearance defects are found.

4. The fully automatic production control system for HDMI high-definition transmission lines according to claim 1 is characterized in that: The intelligent scheduling platform integrates genetic algorithm and particle swarm optimization algorithm, comprehensively considers the urgency of production orders, raw material inventory status and production line load balance, and generates the optimal production sequence.

5. The fully automatic production control system for HDMI high-definition transmission lines according to claim 1 is characterized in that: The cloud-based data analysis center collects historical production data, predicts future production trends through machine learning models, and guides long-term planning.

6. The fully automatic production control system for HDMI high-definition transmission lines according to claim 1 is characterized in that: The comprehensive production line status monitoring module integrates an advanced Internet of Things (IoT) sensor array and edge computing nodes to achieve pre-processing and preliminary analysis of real-time data streams, reduce the burden on data centers and accelerate fault location and troubleshooting.

7. The fully automatic production control system for HDMI high-definition transmission lines according to claim 6 is characterized in that: When the edge computing node receives an abnormal signal, it starts the built-in preset rule engine to determine whether it is an accidental event. If it is confirmed to be a suspected failure mode, it automatically uploads the abnormal sample to the cloud for in-depth analysis and activates the safety interlocking mechanism of the nearby workstation.

8. The fully automatic production control system for HDMI high-definition transmission lines according to claim 1 is characterized in that: It also includes multimodal inspection systems, including optical, acoustic and mechanical sensor groups, which can instantly verify product performance at every stage of the production process and take immediate intervention measures for semi-finished products that do not meet specifications.

9. The fully automatic production control system for HDMI high-definition transmission lines according to claim 8 is characterized in that: The multimodal inspection system redirects semi-finished products that do not meet specifications to a repair station or marks them for manual re-inspection.

10. The fully automatic production control system of HDMI high-definition transmission line according to claim 1 is characterized in that: The intelligent scheduling platform supports graphical interface programming, simplifies the operation process, and improves the human-computer interaction experience.