Industry 4.0 strategy is coming, how to build a route suitable for its own industry 4.0? First talk about the factory-end industry 4.0 core article: logistics. Through years of experience in AGV logistics automation implementation, we share with you: The main content of the logistics data platform: (The following is based on the AGV system, based on the AGV logistics node data interconnection, creating a low-cost automated logistics solution)
Definition of terms: If a certain state of "thing" is clearly defined, its connotation, extension, and even its handling are established.
Template model: Establish specifications so that they can be processed quickly. Such as: using parametric drawing technology.
Transform relationship: for example, a work order, and generate a different time period feed order, which is the relationship between the two.
Data Relationships: For relational databases, various couplings establish an enterprise's data dictionary or data table.
Coding system: In order to improve data standardization, improve data utilization efficiency and timeliness.
Task framework: Starting from the top level, using data methods, combined with software applications and AGV hardware executors, build an overall system framework.
Software: In the enterprise, application software can greatly improve the ability of data processing, reduce errors and improve quality. For example, an enterprise has designed a standardized data dictionary type field, so that the user adopts the selective input method when inputting, so that the data input error and the incomprehensible situation are greatly reduced. In the construction of the production BOM, because the design is a task allocation strategy, and according to the rules, the software system adopts the recommendation and AGV automatic matching, and the overall configuration speed is increased by more than 60%. The problem that needs to be paid attention to is the dynamic transformation of data. Many softwares have unplated problems. Users need to improve the data system, process the data, optimize the knowledge base, and finally become an advanced intelligent system suitable for itself. .
At the same time, it is very difficult to ensure stable operation of the data management system. Enterprises need to strengthen from the aspects of corporate culture, rules and regulations, operational tools, and employee training. Therefore, the principle we developed is that all employees who need to do it are very simple in design, and they are complicated by the system calculations that employees do. It should be specially stated that the data has different sizes and value forms. We need to optimize with specific services to ensure the effectiveness of the node operation and improve the leanness of the overall process.
