Abstraction Module for Cross-Machine Data Analysis
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Solution Overview
Problem
Conventional production processes fail to effectively utilize the extensive data recorded from machines, leading to wasted information that could improve production efficiency and automation.
Innovation Solution
A method that converts operating data from multiple machines with different formats into a uniform abstraction data format, allowing for centralized or decentralized analysis and storage, enabling cross-machine evaluation and improved automation by assessing the operating state of machines and identifying potential errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If operating data from multiple machines with different data formats are collected and used only for controlling respective machines, then machine control functionality is maintained, but data utilization efficiency is low and production optimization potential is lost
Solution Approach 1:
The patent transforms heterogeneous operating data from multiple machines by changing the data format parameter through abstraction modules. Each abstraction module converts machine-specific data formats into a standardized abstraction data format, enabling unified analysis while preserving the original data characteristics for individual machine control.
Solution Approach 2:
The patent introduces abstraction modules as intermediary components between the diverse machine data sources and the central analysis system. These modules act as mediators that translate and standardize data from different formats without requiring changes to the original machines or their control systems.
2Adaptability or versatility
If data is converted into abstraction data with uniform format and stored centrally, then cross-machine analysis capability is improved, but data storage and processing complexity increases
Solution Approach 1:
The patent segments the data processing system into distributed abstraction modules that can be deployed across multiple machines or locations. Each module independently handles data conversion for its associated machine, reducing the complexity burden on any single processing unit while enabling comprehensive cross-machine analysis through centralized storage.
3Extent of automation
If abstraction modules are used to convert operating data into uniform abstraction data, then automation degree is significantly increased, but system implementation complexity increases
Solution Approach 1:
The patent designs abstraction modules with universal functionality that can handle multiple data formats and machine types through a standardized interface. This multi-functional approach allows the same abstraction module architecture to serve different machines and data sources, reducing overall system complexity while maximizing automation potential.
4Reliability
If analysis is performed based on operating data from at least two machines, then error detection and production optimization are improved, but analysis complexity and computational requirements increase
Solution Approach 1:
The patent simplifies cross-machine analysis by transforming diverse operating data into standardized abstraction data with unified parameters and structures. This parameter standardization enables direct comparison and correlation analysis between machines without requiring complex data transformation logic during the analysis phase.
Data Source
Figure 1
AI summary
The invention relates to a method for the cross-machine use of data, in which: - operating data from several machines are acquired by several sensors, wherein the operating data have different first data formats, the first data format depending on the respective sensor; - the operating data are converted into abstraction data by at least one abstraction module, wherein the abstraction data have a uniform second data format and are stored in a central storage device; - an analysis of the abstraction data is performed, wherein the analysis is based on operating data from at least two machines; - an operating state of one of the machines is assessed based on the analysis; and - the analysis of the abstraction data is output.