Intelligent feeding system for edge super-fusion internal mixing

Abstract

The utility model relates to an edge hyper-fusion internal mixing intelligent feeding system, and belongs to the field of production equipment. The system is provided with a warehousing system and an MES system, the warehousing system and the MES system are in communication connection with an edge computing system and a remote monitoring system through the Ethernet, and the edge computing system achieves control scheduling of an intelligent AGV group 6 through wireless connection. Manual intervention is reduced, production efficiency is improved, labor cost is reduced, and stability of a production line is guaranteed.

Description

Technical Field

[0001] The utility model relates to the field of tire production equipment, in particular to an edge super-fusion mixing intelligent feeding system. Background Art

[0002] As we all know, the material transportation process in the mixing process usually relies on traditional manual or semi-automatic methods. Operators manually or semi-automatically extract and transport materials according to the production plan. This process may include manual handling, mechanical assistance and other methods to meet the needs of the production line.

[0003] In the existing production environment, although the MES system has been deeply integrated into the mixing work and can integrate information to a certain extent, there is still information opacity in certain links, such as the material transportation process. At the same time, the existence of manual operations may lead to data entry errors, and even some key information may be ignored during high-intensity work, affecting the accuracy and consistency of material transportation. The existence of these problems makes it difficult for decision makers to accurately understand the status of the entire material supply chain, affecting the accuracy and timeliness of decision-making.

[0004] In the current mixing process, the reliance on manual or semi-automatic material transportation has led to a decrease in production efficiency. Manual operation increases the operation time, and the differences between operators lead to fluctuations in production efficiency, which is less efficient than the automated system. In addition, the discontinuity of manual operation, such as rest or shift handover, leads to discontinuous material transportation, which may cause the production line to stagnate, especially in a highly automated environment. At the same time, manual operation increases the error rate, brings high labor costs, requires a lot of manpower training and management, and further increases the overall production cost.

[0005] In the existing automation system, the automation equipment system is still limited to a single function and cannot flexibly respond to various changes in a complex production environment. For example, under highly customized production requirements, traditional automation systems are difficult to adapt to changing material specifications and production plans. In addition, the system's response speed and accuracy are also facing challenges. It cannot meet the needs of high-speed operation of the production line, which may lead to stagnation and waste of the production line. Summary of the invention

[0006] In order to overcome the shortcomings of the prior art, the utility model provides an edge super-fusion mixing intelligent feeding system, which solves the problems of low efficiency and high cost of manual material transportation in traditional production.

[0007] The technical solution adopted by the utility model to solve its technical problems is: an edge hyper-converged mixing intelligent feeding system, which is equipped with a warehousing system and an MES system, and is characterized in that the warehousing system and the MES system communicate with the edge computing system and the remote monitoring system via Ethernet, and the edge computing system realizes the control and scheduling of the intelligent AGV group 6 via wireless connection.

[0008] The beneficial effects of the utility model are reducing manual intervention, improving production efficiency, reducing labor costs, and ensuring the stability of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The utility model is further described below in conjunction with the accompanying drawings and embodiments.

[0010] Figure 1 A schematic structural diagram of the utility model.

[0011] In the figure, 1. Warehousing system, 2. MES system, 3. Edge computing system, 4. Ethernet, 5. Remote monitoring system, 6. Smart AGV group. DETAILED DESCRIPTION

[0012] In the figure, the utility model is provided with a storage system 1 and an MES system 2, which communicate with an edge computing system 3 and a remote monitoring system 5 via Ethernet 4, and the edge computing system 3 realizes the control and dispatch of the intelligent AGV group 6 via wireless connection. The storage system 1 and the MES system 2 transmit the production plan, workshop capacity, production status, and storage information to the edge computing system 3 via Ethernet 4; the edge computing system 3 formulates a pre-feeding plan based on the information transmitted by the storage system 1 and the MES system 2 and the operation status of the intelligent AGV group 6, and sends it to the corresponding intelligent AGV group 6; the intelligent AGV group 6 accepts and executes the issued plan, and feeds back the transportation implementation status to the edge computing system 3; the edge computing system 3 feeds back the real-time operation status to the storage system 1, the MES system 2, and the remote monitoring system 5; the remote monitoring system 5 obtains the real-time feeding information through Ethernet 4, and makes the next production decision based on the actual situation, and feeds back to the MES system 2.

[0013] The warehousing system 1 is a software and hardware system for managing and controlling the storage, organization and distribution of materials in the warehouse. Its working principle includes collecting, recording, tracking and managing material information, and sharing information with other systems through network interfaces. The MES system 2 is an information system for monitoring and controlling the manufacturing process. It connects the production plan with the manufacturing process and is used to track and record various data and events in the production process to optimize, manage and trace the production process. Its working principle includes production planning, production process monitoring, process management, quality traceability and other functions. The edge operating system 3 is an operating system for edge computing environments. Edge computing is a distributed computing model that extends data processing and storage functions from traditional centralized data centers to edge devices close to data sources, integrating computing, storage, network and other resources at the edge and working together. The remote monitoring system 5 monitors and manages the operating status and data of equipment, systems or environments in real time through network communication. The intelligent AGV group 6 is a system composed of multiple automatic guided vehicles (AGVs), equipped with various sensors, navigation equipment and control systems, which can independently complete material handling and transportation tasks. The intelligent automatic guided vehicle group is a group of autonomous navigation automated transportation systems. By carrying sensors and navigation equipment, it can intelligently perform material transportation tasks within a factory or warehouse.

[0014] The edge computing system 3 is deployed at the edge computing node of the production site, responsible for local data processing and real-time decision-making, and responsible for implementing the scheduling system of the intelligent AGV. It can establish prediction models and intelligent algorithms based on the current order situation, previous production capacity data and other real-time data to optimize the feeding plan. The intelligent AGV (automatic guided vehicle) is equipped with various sensors and positioning systems, and can navigate autonomously in the factory. The edge computing system 3 issues the feeding task of the intelligent AGV, and performs intelligent path planning based on the real-time order requirements and the status of the production workshop to ensure that the feeding task is completed in a timely and accurate manner. The real-time operation status, sensor data and environmental data of the AGV are transmitted back to the edge computing system 3 through the wireless network. These data include the location, speed, power, cargo status and other information of the trolley. The edge computing system 3 analyzes these data to understand the operation status of the trolley in real time. The edge computing system 3 adjusts the feeding plan in real time based on the data received from the MES system 2 and the storage system 1. It can predict the arrival time of the trolley, identify potential failure risks, and avoid production line stagnation caused by trolley failure. At the same time, it can also optimize the driving path of the trolley according to the current status and historical data of the MES system 2 to improve the feeding efficiency. The edge computing system 3 provides remote monitoring and management functions. Production managers can monitor the operating status of the trolley in real time, view real-time data, and adjust the feeding plan through the remote monitoring system 5 through the cloud interface to ensure the stable operation of the MES system 2.

[0015] Real-time response and intelligent decision-making of the utility model: combined with edge computing, the system can respond to order requirements in real time, make intelligent decisions based on real-time data, and ensure timely and accurate material transportation. Intelligent path planning and obstacle avoidance technology: using edge computing and sensor data to realize path planning and obstacle avoidance of intelligent AGV, improve transportation efficiency, and reduce the risk of collision and blockage. Remote monitoring and real-time feedback: through the cloud interface, production management personnel can monitor the system operation status at any time, understand the location and task status of AGV in real time, and provide a high degree of visualization and remote control. Fault prediction and prevention: using real-time data analysis, the system can predict the risk of AGV failure, take measures in advance, avoid the problem of untimely delivery caused by AGV failure, and improve the reliability and stability of the system. High resource integration and cost savings: the hyper-converged architecture integrates computing, storage, network and other functions into one node, reducing hardware costs, improving resource utilization, and making the system more compact and efficient.

Claims

1. An edge hyper-fusion mixing intelligent feeding system, equipped with a storage system and an MES system, characterized in that: The warehousing system and the MES system communicate with the edge computing system and the remote monitoring system via Ethernet, and the edge computing system realizes the control and scheduling of the intelligent AGV group 6 via wireless connection.

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