Lampo establishes a more intelligent, flexible and agile operation system, relying on information digitization and MES system data flow, to manage and control production resources, production equipment, production facilities and production processes in a fine, agile, flexible and efficient way, to enhance the comprehensive utilization rate of OEE production to improve the ability of fine control and to shorten the effective manufacturing cycle of DTD, so that the whole process of order supervision, production and operation intelligent analysis can be realized. operation intelligent analysis can be realized.
Big data integration is the organic concentration of data from different sources, formats and characteristics, which includes both logical and physical forms. Big data cleansing is to re-examine and verify the data centralized in the platform, to find and correct recognizable errors, and to deal with invalid and missing values, so as to obtain clean and consistent data.
Distributed storage, cloud storage and other technologies are used to store and manage data economically, securely and reliably, prioritize data, and use high throughput database technology and unstructured access technology to support efficient and fast access to data in the cloud system.
Discover valuable and potentially useful information and knowledge implicit in large, incomplete, fuzzy and random databases.
Using computer graphics and image processing technology including 2D comprehensive report, VR/AR and other visualization display technology, the data is converted into graphics and images and displayed on the screen, making the boring and abstract data intuitive and easy to understand, and realizing the analysis, communication and decision-making support based on the visualized data through interactive processing.
Includes standards and specifications for general big data technologies, platforms, products, industries, security and other aspects of the industrial Internet, involving data specification, standards, control, supervision and other technologies.
It involves the security of big data collection, transmission, storage, mining, distribution and application, as well as user control, data traceability, privacy data protection and security situational awareness.
Big data integration is the organic concentration of data from different sources, formats and characteristics, which includes both logical and physical forms. Big data cleansing is to re-examine and verify the data centralized in the platform, to find and correct recognizable errors, and to deal with invalid and missing values, so as to obtain clean and consistent data.
Distributed storage, cloud storage and other technologies are used to store and manage data economically, securely and reliably, prioritize data, and use high throughput database technology and unstructured access technology to support efficient and fast access to data in the cloud system.
Discover valuable and potentially useful information and knowledge implicit in large, incomplete, fuzzy and random databases.
Using computer graphics and image processing technology including 2D comprehensive report, VR/AR and other visualization display technology, the data is converted into graphics and images and displayed on the screen, making the boring and abstract data intuitive and easy to understand, and realizing the analysis, communication and decision-making support based on the visualized data through interactive processing.
Includes standards and specifications for general big data technologies, platforms, products, industries, security and other aspects of the industrial Internet, involving data specification, standards, control, supervision and other technologies.
It involves the security of big data collection, transmission, storage, mining, distribution and application, as well as user control, data traceability, privacy data protection and security situational awareness.
Low-cost new data processing technology and cloud computing can realize comprehensive and continuous data collection and long-time storage, and can obtain massive data can be processed and analyzed to provide support for decision-making.
Through the high degree of integration of information and data visualization of the whole production process, the information transparency of the production process is realized, and the total dispatch center of the enterprise can fully grasp the production process, the actual state of the product and the optional path to the target state.
Through the collection of various types of data on equipment, including equipment operating status parameters, working condition data, environmental parameters, maintenance records, etc., the use of data for a long period of time constitutes the basis for big data analysis. Using algorithms and models, equipment fault diagnosis and fault prediction are realized through data analysis and processing.
On the basis of fault diagnosis and prediction, machines, data and production indicators form an intertwined network, and through real-time interaction and adjustment of information, coupled with optimization criteria, they are compared, evaluated, and ultimately the best solution is selected.
